Jian Pei

Bio

Data science, data mining, databases, information retrieval, computational statistics, applied machine learning and AI.

Education

• Ph.D. Simon Fraser University, 2002

Positions

• Arthur S. Pearse Distinguished Professor of Computer Science
• Professor of Computer Science
• Chair of the Department of Computer Science
• Professor of Electrical and Computer Engineering
• Professor of Biostatistics & Bioinformatics

Courses Taught

• ECE 899: Special Readings in Electrical Engineering
• ECE 590: Advanced Topics in Electrical and Computer Engineering
• ECE 583: Data Science
• COMPSCI 590: Advanced Topics in Computer Science
• COMPSCI 526: Data Science
• COMPSCI 394: Research Independent Study
• COMPSCI 393: Research Independent Study
• COMPSCI 391: Independent Study
• CBB 590: Special Topics in Computational Biology
• CBB 526: Data Science
• BRAINSOC 795T: Bass Connections in Brain & Society Research Team
• BRAINSOC 395T: Bass Connections in Brain & Society Research Team

Publications

• Zhu F, Pei J, Zeller M, Zhang B. The Fourth International Workshop on Smart Data for Blockchain and Distributed Ledger (SDBD'24). In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2024. p. 6757–8.
• Shen K, Wu L, Tang S, Xu F, Long B, Zhuang Y, et al. Ask Questions with Double Hints: Visual Question Generation with Answer-awareness and Region-reference. IEEE transactions on pattern analysis and machine intelligence. 2024 Jul;PP.
• Luo X, Pei J. Applications and Computation of the Shapley Value in Databases and Machine Learning. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2024. p. 630–5.
• Zhang J, Xue R, Fan W, Xu X, Li Q, Pei J, et al. Linear-Time Graph Neural Networks for Scalable Recommendations. In: WWW 2024 - Proceedings of the ACM Web Conference. 2024. p. 3533–44.
• Cong Z, Shi B, Li S, Yang J, He Q, Pei J. FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently. IEEE Transactions on Knowledge and Data Engineering. 2024 Apr 1;36(4):1537–51.
• Zhang M, Pei J. Protecting Data Buyer Privacy in Data Markets. IEEE Internet Computing. 2024 Jan 1;28(4):14–20.
• Sun Q, Zhang J, Liu J, Xiong L, Pei J, Ren K. Shapley Value Approximation Based on Complementary Contribution. IEEE Transactions on Knowledge and Data Engineering. 2024 Jan 1;
• Huang Y, Sun L, Wang H, Wu S, Zhang Q, Li Y, et al. Position: TRUSTLLM: Trustworthiness in Large Language Models. In: Proceedings of Machine Learning Research. 2024. p. 20166–270.
• Xing N, Cai S, Chen G, Luo Z, Chin Ooi B, Pei J. Database Native Model Selection: Harnessing Deep Neural Networks in Database Systems. In: Proceedings of the VLDB Endowment. 2024. p. 1020–33.
• Zhuang S, Shou L, Pei J, Gong M, Ren H, Zuccon G, et al. Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval. In: SIGIR-AP 2023 - Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region. 2023. p. 212–22.
• Wu N, Gong M, Shou L, Pei J, Jiang D. RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation. In: International Conference on Information and Knowledge Management, Proceedings. 2023. p. 4871–8.
• Shi H, Tayebi MA, Pei J, Cao J. Cost-Sensitive Learning for Medical Insurance Fraud Detection With Temporal Information. IEEE Transactions on Knowledge and Data Engineering. 2023 Oct 1;35(10):10451–63.
• Jiang H, Yu H, Cheng X, Pei J, Pless R, Yu J. DP2-Pub: Differentially Private High-Dimensional Data Publication With Invariant Post Randomization. IEEE Transactions on Knowledge and Data Engineering. 2023 Oct 1;35(10):10831–44.
• Wu L, Pei J, Tang J, Xia Y, Guo X. Deep Learning on Graphs: Methods and Applications (DLG-KDD2023). In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2023. p. 5891–2.
• Wu X, Hu Z, Pei J, Huang H. Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2023. p. 2648–59.
• Wu L, Cui P, Pei J, Zhao L, Guo X. Graph Neural Networks: Foundation, Frontiers and Applications. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2023. p. 5831–2.
• Liu C, Zhou Z, Pei J, Zhang Y, Shi Y. Decentralized Composite Optimization in Stochastic Networks: A Dual Averaging Approach with Linear Convergence. IEEE Transactions on Automatic Control. 2023 Aug 1;68(8):4650–65.
• Wu S, Zhao R, Zheng Y, Pei J, Liu B. Identify Event Causality with Knowledge and Analogy. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023. 2023. p. 13745–53.
• Xu Z, Shou L, Pei J, Gong M, Su Q, Quan X, et al. A Graph Fusion Approach for Cross-Lingual Machine Reading Comprehension. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023. 2023. p. 13861–8.
• Zhang Z, Niu C, Cui P, Pei J, Zhang B, Zhu W. Permutation-Equivariant and Proximity-Aware Graph Neural Networks With Stochastic Message Passing. IEEE Transactions on Knowledge and Data Engineering. 2023 Jun 1;35(6):6182–93.
• Xia L, Huang C, Xu Y, Pei J. Multi-Behavior Sequential Recommendation With Temporal Graph Transformer. IEEE Transactions on Knowledge and Data Engineering. 2023 Jun 1;35(6):6099–112.
• Xu J, Hong N, Xu Z, Zhao Z, Wu C, Kuang K, et al. Data-Driven Learning for Data Rights, Data Pricing, and Privacy Computing. Engineering. 2023 Jun 1;25:66–76.
• Fionda V, Hartig O, Abdolazimi R, Amer-Yahia S, Chen H, Chen X, et al. Tutorials at The Web Conference 2023. In: Companion Proceedings of the ACM Web Conference 2023. ACM; 2023. p. 648–58.
• Peng J, Zou H, Liu J, Li S, Jiang Y, Pei J, et al. Offline Policy Evaluation in Large Action Spaces via Outcome-Oriented Action Grouping. In: ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023. 2023. p. 1220–30.
• Huang R, Wang J, Song S, Lin X, Zhu X, Pei J. Efficiently Cleaning Structured Event Logs: A Graph Repair Approach. ACM Transactions on Database Systems. 2023 Mar 14;48(1).
• Zhang Z, Cui P, Pei J, Wang X, Zhu W. Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs. IEEE Transactions on Knowledge and Data Engineering. 2023 Mar 1;35(3):2544–55.
• Zhou C, Li Q, Li C, Yu J, Liu Y, Wang G, et al. A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT. 2023.
• Zhang J, Xia H, Sun Q, Liu J, Xiong L, Pei J, et al. Dynamic Shapley Value Computation. In: Proceedings - International Conference on Data Engineering. 2023. p. 639–52.
• Li H, Cao H, Feng Y, Li X, Pei J. Optimization of Graph Clustering Inspired by Dynamic Belief Systems. IEEE Transactions on Knowledge and Data Engineering. 2023 Jan 1;
• Chen N, Shou L, Song T, Gong M, Pei J, Chang J, et al. Structural Contrastive Pretraining for Cross-Lingual Comprehension. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2023. p. 2042–57.
• Zou H, Wang H, Xu R, Li B, Pei J, Ye J, et al. Factual Observation Based Heterogeneity Learning for Counterfactual Prediction. In: Proceedings of Machine Learning Research. 2023. p. 350–70.
• Xue R, Han H, Torkamani MA, Pei J, Liu X. LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation. In: Proceedings of Machine Learning Research. 2023. p. 38926–37.
• Chen N, Shou L, Pei J, Gong M, Cao B, Chang J, et al. Alleviating Over-smoothing for Unsupervised Sentence Representation. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2023. p. 3552–66.
• Wang H, Zhu Q, Pei J. Clinical Assessment of Pneumocystosis with MIMIC Data. In: Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. 2023. p. 2751–3.
• Pei J, Fernandez RC, Yu X. Data and AI Mo del Markets: Opportunities for Data and Model Sharing, Discovery, and Integration. In: Proceedings of the VLDB Endowment. 2023. p. 3872–3.
• Le TD, Li J, Ness R, Triantafillou S, Shimizu S, Cui P, et al. Preface: The 2023 ACM SIGKDD Workshop on Causal Discovery, Prediction and Decision. In: Proceedings of Machine Learning Research. 2023. p. 1–2.
• Jiang H, Pei J, Yu D, Yu J, Gong B, Cheng X. Applications of Differential Privacy in Social Network Analysis: A Survey. IEEE Transactions on Knowledge and Data Engineering. 2023 Jan 1;35(1):108–27.
• Xia L, Shao Y, Huang C, Xu Y, Xu H, Pei J. Disentangled Graph Social Recommendation. In: Proceedings - International Conference on Data Engineering. 2023. p. 2332–44.
• Kang J, Zhang S, Li B, He J, Pei J, Zhou D. TrustLOG: The First Workshop on Trustworthy Learning on Graphs. In: International Conference on Information and Knowledge Management, Proceedings. 2022. p. 5169–70.
• Pei J. A Survey on Data Pricing: From Economics to Data Science. IEEE Transactions on Knowledge and Data Engineering. 2022 Oct 1;34(10):4586–608.
• Wu L, Pei J, Tang J, Xia Y, Guo X. Deep Learning on Graphs: Methods and Applications (DLG-KDD2022). In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2022. p. 4906–7.
• Wu L, Cui P, Pei J, Zhao L, Guo X. Graph Neural Networks: Foundation, Frontiers and Applications. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2022. p. 4840–1.
• Zhang Y, Gao S, Pei J, Huang H. Improving Social Network Embedding via New Second-Order Continuous Graph Neural Networks. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2022. p. 2515–23.
• Cong Z, Luo X, Pei J, Zhu F, Zhang Y. Data pricing in machine learning pipelines. Knowledge and Information Systems. 2022 Jun 1;64(6):1417–55.
• Zhang Y, Wu L, Shen Q, Pang Y, Wei Z, Xu F, et al. Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation. In: WWW 2022 - Proceedings of the ACM Web Conference 2022. 2022. p. 2153–62.
• Zhang Y, Gao H, Pei J, Huang H. Robust Self-Supervised Structural Graph Neural Network for Social Network Prediction. In: WWW 2022 - Proceedings of the ACM Web Conference 2022. 2022. p. 1352–61.
• Pang Y, Wu L, Shen Q, Zhang Y, Wei Z, Xu F, et al. Heterogeneous global graph neural networks for personalized session-based recommendation. In: WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining. 2022. p. 775–83.
• Li J, Pei J, Huang H. Communication-Efficient Robust Federated Learning with Noisy Labels. arXiv preprint arXiv:220605558. 2022;
• Huang F, Gao S, Pei J, Huang H. Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization. Journal of Machine Learning Research. 2022 Jan 1;23.
• Singh G, Chu L, Wang L, Pei J, Tian Q, Zhang Y. Mining Minority-Class Examples with Uncertainty Estimates. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2022. p. 258–71.
• Liang S, Shou L, Pei J, Gong M, Zuo W, Zuo X, et al. Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding. arXiv preprint arXiv:220503656. 2022;
• Zhang H, Wu B, Yuan X, Pan S, Tong H, Pei J. Trustworthy Graph Neural Networks: Aspects, Methods and Trends. arXiv preprint arXiv:220507424. 2022;
• Luo X, Pei J, Cong Z, Xu C. On Shapley Value in Data Assemblage Under Independent Utility. In: Proceedings of the VLDB Endowment. 2022. p. 2761–73.
• Wu L, Cui P, Pei J, Zhao L. Graph Neural Networks: Foundations, Frontiers, and Applications. 2022.
• Han J, Pei J, Tong H. Data Mining: Concepts and Techniques, Fourth Edition. In 2022. p. 1–752.
• Lin W, Shou L, Gong M, Jian P, Wang Z, Byrne B, et al. Combining Unstructured Content and Knowledge Graphs into Recommendation Datasets. In: CEUR Workshop Proceedings. 2022. p. 45–52.
• Liang S, Shou L, Pei J, Gong M, Zuo W, Zuo X, et al. Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022. 2022. p. 9903–18.
• Ren H, Shou L, Pei J, Wu N, Gong M, Jiang D. Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval. In: Findings of the Association for Computational Linguistics: EMNLP 2022. 2022. p. 444–59.
• Charette L, Chu L, Chen Y, Pei J, Wang L, Zhang Y. Cosine Model Watermarking Against Ensemble Distillation. arXiv preprint arXiv:220302777. 2022;
• Cong Z, Luo X, Pei J, Zhu F, Zhang Y. Data pricing in machine learning pipelines. Knowledge and Information Systems. 2022;1–39.
• Zhuang S, Ren H, Shou L, Pei J, Gong M, Zuccon G, et al. Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation. arXiv preprint arXiv:220610128. 2022;
• Chen N, Shou L, Gong M, Pei J. From good to best: Two-stage training for cross-lingual machine reading comprehension. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2022. p. 10501–8.
• Wu L, Cui P, Pei J, Zhao L, Song L. Graph neural networks. In: Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, Singapore; 2022. p. 27–37.
• Bajaj M, Chu L, Romaniello V, Singh G, Pei J, Zhou Z, et al. Revealing Unfair Models by Mining Interpretable Evidence. arXiv preprint arXiv:220705811. 2022;
• Huang F, Gao S, Pei J, Huang H. Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization. J Mach Learn Res. 2022;23:36–1.
• Li Z, Huang C, Xia L, Xu Y, Pei J. Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction. arXiv preprint arXiv:220408587. 2022;
• Chen N, Shou L, Gong M, Pei J, Jiang D. Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling. arXiv preprint arXiv:220405210. 2022;
• Fan Z, Fang H, Zhou Z, Pei J, Friedlander MP, Zhang Y. Fair and efficient contribution valuation for vertical federated learning. arXiv preprint arXiv:220102658. 2022;
• Zhao L, Wu L, Cui P, Pei J. Representation Learning. In: Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, Singapore; 2022. p. 3–15.
• Shi H, Yang Y, Wang L, Ma D, Beg MF, Pei J, et al. Two-Dimensional Functional Principal Component Analysis for Image Feature Extraction. Journal of Computational and Graphical Statistics. 2022 Jan 1;31(4):1127–40.
• Cui P, Wu L, Pei J, Zhao L, Wang X. Graph Representation Learning. In: Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, Singapore; 2022. p. 17–26.
• Lin W, Shou L, Gong M, Jian P, Wang Z, Byrne B, et al. Transformer-Empowered Content-Aware Collaborative Filtering. arXiv preprint arXiv:220400849. 2022;
• Zhang Y, Bao R, Pei J, Huang H. Toward Unified Data and Algorithm Fairness via Adversarial Data Augmentation and Adaptive Model Fine-tuning. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2022. p. 1317–22.
• Liu N, Wang X, Bo D, Shi C, Pei J. Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum. In: Advances in Neural Information Processing Systems. 2022.
• Hu X, Chu L, Pei J, Liu W, Bian J. Model complexity of deep learning: a survey. Knowledge and Information Systems. 2021 Oct 1;63(10):2585–619.
• Shou L, Gong M, Pei J, Geng X, Zhou X, Jiang D. Language Scaling. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. ACM; 2021. p. 4068–9.
• Zhou Z, Chu L, Liu C, Wang L, Pei J, Zhang Y. Towards Fair Federated Learning. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2021. p. 4100–1.
• Zhu F, Pei J. The Third International Workshop on Smart Data for Blockchain and Distributed Ledger (SDBD2021): Joint Workshop with SIGKDD 2021 Trust Day. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2021. p. 4185–6.
• Zhang Q, Gu B, Deng C, Gu S, Bo L, Pei J, et al. AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2021. p. 3917–27.
• Liang S, Gong M, Pei J, Shou L, Zuo W, Zuo X, et al. Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2021. p. 3231–9.
• Wu L, Tang J, Xia Y, Pei J, Guo X. The Sixth International Workshop on Deep Learning on Graphs - Methods and Applications (DLG-KDD'21). In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2021. p. 4167–8.
• Pei J, Zhu F, Cong Z, Luo X, Liu H, Mu X. Data Pricing and Data Asset Governance in the AI Era. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2021. p. 4058–9.
• Banitalebi-Dehkordi A, Vedula N, Pei J, Xia F, Wang L, Zhang Y. Auto-Split: A General Framework of Collaborative Edge-Cloud AI. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2021. p. 2543–53.
• Liang S, Shou L, Pei J, Gong M, Zuo W, Jiang D. CalibreNet: Calibration Networks for Multilingual Sequence Labeling. In: WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 2021. p. 842–50.
• Yu W, He X, Pei J, Chen X, Xiong L, Liu J, et al. Visually aware recommendation with aesthetic features. VLDB Journal. 2021 Jul 1;30(4):495–513.
• Liu J, Xiong L, Pei J, Luo J, Zhang H, Yu W. Group-Based Skyline for Pareto Optimal Groups. IEEE Transactions on Knowledge and Data Engineering. 2021 Jul 1;33(7):2914–29.
• Wang P, Zheng W, Wang J, Pei J. Automating entity matching model development. In: Proceedings - International Conference on Data Engineering. 2021. p. 1296–307.
• Liu J, Xiong L, Zhang Q, Pei J, Luo J. Eclipse: Generalizing kNN and skyline. In: Proceedings - International Conference on Data Engineering. 2021. p. 972–83.
• Hu X, Chu L, Pei J, Liu W, Bian J. Model Complexity of Deep Learning: A Survey. 2021.
• Yang Y, Pei J. Influence Analysis in Evolving Networks: A Survey. IEEE Transactions on Knowledge and Data Engineering. 2021 Mar 1;33(3):1045–63.
• Shi H, Ma D, Nie Y, Faisal Beg M, Pei J, Cao J, et al. Early diagnosis of Alzheimer's disease on ADNI data using novel longitudinal score based on functional principal component analysis. Journal of medical imaging (Bellingham, Wash). 2021 Mar;8(2):024502.
• Liu J, Lou J, Xiong L, Pei J, Sun J. Dealer: An end-to-end model marketplace with differential privacy. Proceedings of the VLDB Endowment. 2021 Jan 1;14(6):957–69.
• Yuan F, Shou L, Pei J, Lin W, Gong M, Fu Y, et al. Reinforced Multi-Teacher Selection for Knowledge Distillation. In: 35th AAAI Conference on Artificial Intelligence, AAAI 2021. 2021. p. 14284–91.
• Chu L, Wang L, Dong Y, Pei J, Zhou Z, Zhang Y. Fedfair: Training fair models in cross-silo federated learning. arXiv preprint arXiv:210905662. 2021;
• Xu C, Zhang C, Xu J, Pei J. SlimChain: scaling blockchain transactions through off-chain storage and parallel processing. Proceedings of the VLDB Endowment. 2021;14:2314–26.
• Xia L, Huang C, Xu Y, Dai P, Zhang X, Yang H, et al. Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation. In: 35th AAAI Conference on Artificial Intelligence, AAAI 2021. 2021. p. 4486–93.
• Lin Q, Zhang J, Liu J, Ren K, Lou J, Xiong L, et al. Demonstration of dealer: An end-to-end model marketplace with differential privacy. In: Proceedings of the VLDB Endowment. 2021. p. 2747–50.
• Lam PC-H, Chu L, Torgonskiy M, Pei J, Zhang Y, Wang L. Finding representative interpretations on convolutional neural networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021. p. 1345–54.
• Shou L, Gong M, Pei J, Geng X, Zhou X, Jiang D. Language Scaling: Applications, Challenges and Approaches. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021. p. 4068–9.
• Zhou Y, Geng X, Shen T, Pei J, Zhang W, Jiang D. Modeling Event-Pair Relations in External Knowledge Graphs for Script Reasoning. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. 2021. p. 4586–96.
• Lam PCH, Chu L, Torgonskiy M, Pei J, Zhang Y, Wang L. Finding Representative Interpretations on Convolutional Neural Networks. In: Proceedings of the IEEE International Conference on Computer Vision. 2021. p. 1325–34.
• Wang H, Xu C, Zhang C, Xu J, Peng Z, Pei J. vChain+: Optimizing Verifiable Blockchain Boolean Range Queries (Technical Report). 2021;
• Cong Z, Chu L, Yang Y, Pei J. Comprehensible counterfactual explanation on Kolmogorov-Smirnov test. In: Proceedings of the VLDB Endowment. 2021. p. 1583–96.
• Bajaj M, Chu L, Xue ZY, Pei J, Wang L, Lam PCH, et al. Robust Counterfactual Explanations on Graph Neural Networks. In: Advances in Neural Information Processing Systems. 2021. p. 5644–55.
• Hu X, Chu L, Pei J, Bian J, Liu W. Deep Learning Model Complexity: Concepts and Approaches. 2021;
• Fan Z, Fang H, Zhou Z, Pei J, Friedlander MP, Liu C, et al. Improving fairness for data valuation in federated learning. arXiv preprint arXiv:210909046. 2021;
• Liu J, Yang J, Xiong L, Pei J, Luo J, Guo Y, et al. Skyline diagram: Efficient space partitioning for skyline queries. IEEE Transactions on Knowledge and Data Engineering. 2021 Jan 1;33(1):271–86.
• Cho-Ho Lam P, Chu L, Torgonskiy M, Pei J, Zhang Y, Wang L. Finding Representative Interpretations on Convolutional Neural Networks. arXiv e-prints. 2021;arXiv-2108.
• Xu C, Zhang C, Xu J, Pei J. Slimchain: Scaling blockchain transactions through off-chain storage and parallel processing. In: Proceedings of the VLDB Endowment. 2021. p. 2314–26.
• Huang Y, Chu L, Zhou Z, Wang L, Liu J, Pei J, et al. Personalized Cross-Silo Federated Learning on Non-IID Data. In: 35th AAAI Conference on Artificial Intelligence, AAAI 2021. 2021. p. 7865–73.
• Wu L, Chen Y, Shen K, Guo X, Gao H, Li S, et al. Graph neural networks for natural language processing: A survey. arXiv preprint arXiv:210606090. 2021;
• Guo Y, Shou L, Pei J, Gong M, Xu M, Wu Z, et al. Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding. In: EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings. 2021. p. 3226–37.
• Liu C, Zhou Z, Shi Y, Pei J, Chu L, Zhang Y. Achieving Model Fairness in Vertical Federated Learning. arXiv preprint arXiv:210908344. 2021;
• Huang H, Geng X, Jian P, Long G, Jiang D. Reasoning over entity-action-location graph for procedural text understanding. In: ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference. 2021. p. 5100–9.
• Cong Z, Chu L, Yang Y, Pei J. Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test. 2020.
• Liu J, Shou L, Pei J, Gong M, Yang M, Jiang D. Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation. 2020.
• Zhang X, Shou L, Pei J, Gong M, Wen L, Jiang D. A Graph Representation of Semi-structured Data for Web Question Answering. 2020.
• Jiang H, Pei J, Yu D, Yu J, Gong B, Cheng X. Applications of Differential Privacy in Social Network Analysis: A Survey. 2020.
• Pei J. A Survey on Data Pricing: from Economics to Data Science. 2020.
• Shou L, Bo S, Cheng F, Gong M, Pei J, Jiang D. Mining Implicit Relevance Feedback from User Behavior for Web Question Answering. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2020. p. 2931–41.
• Wang F, Cui P, Pei J, Song Y, Zang C. Recent Advances on Graph Analytics and Its Applications in Healthcare. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2020. p. 3545–6.
• Hu X, Liu W, Bian J, Pei J. Measuring Model Complexity of Neural Networks with Curve Activation Functions. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2020. p. 1521–31.
• Pei J. Data Pricing - From Economics to Data Science. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2020. p. 3553–4.
• Wang X, Zhu M, Bo D, Cui P, Shi C, Pei J. AM-GCN: Adaptive Multi-channel Graph Convolutional Networks. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2020. p. 1243–53.
• Yu W, Liu J, Pei J, Xiong L, Chen X, Qin Z. Efficient Contour Computation of Group-Based Skyline. IEEE Transactions on Knowledge and Data Engineering. 2020 Jul 1;32(7):1317–32.
• Choi DW, Pei J, Lin X. On spatial keyword covering. Knowledge and Information Systems. 2020 Jul 1;62(7):2577–612.
• Shou L, Bo S, Cheng F, Gong M, Pei J, Jiang D. Mining Implicit Relevance Feedback from User Behavior for Web Question Answering. 2020.
• Ning G, Pei J, Huang H. LightTrack: A generic framework for online top-down human pose tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 2020. p. 4456–65.
• Yang Y, Mao X, Pei J, He X. Continuous influence maximization. ACM Transactions on Knowledge Discovery from Data. 2020 May 8;14(3).
• Amer-Yahia S, Pei J. VLDB SI 2018 editorial. VLDB Journal. 2020 May 1;29(2–3):593–4.
• Cong Z, Chu L, Wang L, Hu X, Pei J. Exact and consistent interpretation of piecewise linear models hidden behind APIs: A closed form solution. In: Proceedings - International Conference on Data Engineering. 2020. p. 613–24.
• Huang F, Gao S, Pei J, Huang H. Momentum-Based policy gradient methods. In: 37th International Conference on Machine Learning, ICML 2020. 2020. p. 4372–83.
• Jiang H, Pei J, Yu D, Yu J, Gong B, Cheng X. Differential privacy and its applications in social network analysis: A survey. arXiv preprint arXiv:201002973. 2020;
• Cong Z, Chu L, Yang Y, Pei J. Comprehensible counterfactual explanation on Kolmogorov-Smirnov test. arXiv preprint arXiv:201101223. 2020;
• Zhang X, Shou L, Pei J, Gong M, Wen L, Jiang D. A graph representation of semi-structured data for web question answering. arXiv preprint arXiv:201006801. 2020;
• Gao S, Huang F, Pei J, Huang H. Discrete model compression with resource constraint for deep neural networks. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2020. p. 1896–905.
• Li H-J, Wang Z, Pei J, Cao J, Shi Y. Optimal estimation of low-rank factors via feature level data fusion of multiplex signal systems. IEEE Transactions on Knowledge and Data Engineering. 2020;
• Liu J, Shou L, Pei J, Gong M, Yang M, Jiang D. Cross-lingual machine reading comprehension with language branch knowledge distillation. arXiv preprint arXiv:201014271. 2020;
• Amer-Yahia S, Pei J. VLDB SI 2018 editorial. The VLDB Journal. 2020;29:593–4.
• Huang Y, Chu L, Zhou Z, Wang L, Liu J, Pei J, et al. Personalized federated learning: An attentive collaboration approach. 2020;
• Gao S, Huang F, Pei J, Huang H. Discrete model compression with resource constraint for deep neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020. p. 1899–908.
• Chu L, Zhang Y, Yang Y, Wang L, Pei J. Online density bursting subgraph detection from temporal graphs. In: Proceedings of the VLDB Endowment. 2020. p. 2353–65.
• Gao S, Huang F, Pei J, Huang H. Discrete model compression with resource constraint. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2020). 2020.
• Huang F, Gao S, Pei J, Huang H. Momentum-based policy gradient methods. In: International conference on machine learning. 2020. p. 4422–33.
• Luo L, Pei J, Huang H. Sinkhorn regression. In: IJCAI International Joint Conference on Artificial Intelligence. 2020. p. 2598–604.
• Lei M, Chu L, Wang Z, Pei J, He C, Zhang X, et al. Mining top-k sequential patterns in transaction database graphs. World Wide Web. 2020;23:103–30.
• Lei M, Chu L, Wang Z, Pei J, He C, Zhang X, et al. Mining top-k sequential patterns in transaction database graphs: A new challenging problem and a sampling-based approach. World Wide Web. 2020 Jan 1;23(1):103–30.
• Liu J, Xiong L, Pei J, Luo J, Zhang H, Zhang S. Skyrec: Finding Pareto optimal groups. In: International Conference on Information and Knowledge Management, Proceedings. 2019. p. 2913–6.
• Yang Y, Wang Z, Jin T, Pei J, Chen E. Tracking top-k influential users with relative errors. In: International Conference on Information and Knowledge Management, Proceedings. 2019. p. 1783–92.
• Pei J. Practicing the art of data science. In: International Conference on Information and Knowledge Management, Proceedings. 2019. p. 7.
• Zhao Z, Chu L, Tao D, Pei J. Classification with label noise: a Markov chain sampling framework. Data Mining and Knowledge Discovery. 2019 Sep 1;33(5):1468–504.
• Yu S, Chen H, Gu B, Pei J, Ning K, Huang H. Tackle balancing constraint for incremental semi-supervised support vector learning. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2019. p. 1587–95.
• Tu K, Ma J, Cui P, Pei J, Zhu W. Autone: Hyperparameter optimization for massive network embedding. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2019. p. 216–25.
• Fan C, Zhang Y, Pan Y, Li X, Zhang C, Yuan R, et al. Multi-horizon time series forecasting with temporal attention learning. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2019. p. 2527–35.
• Gao H, Pei J, Huang H. Progan: Network embedding via proximity generative adversarial network. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2019. p. 1308–16.
• Gao H, Pei J, Huang H. Conditional random field enhanced graph convolutional neural networks. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2019. p. 276–84.
• Huang X, Cui P, Dong Y, Li J, Liu H, Pei J, et al. Learning from networks: Algorithms, theory, and applications. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2019. p. 3221–2.
• Liu J, Yang J, Xiong L, Pei J. Secure and Efficient Skyline Queries on Encrypted Data. IEEE transactions on knowledge and data engineering. 2019 Jul;31(7):1397–411.
• Yu W, Lin X, Zhang W, Pei J, McCann JA. SimRank*: effective and scalable pairwise similarity search based on graph topology. VLDB Journal. 2019 Jun 1;28(3):401–26.
• Cui P, Wang X, Pei J, Zhu W. A Survey on Network Embedding. IEEE Transactions on Knowledge and Data Engineering. 2019 May 1;31(5):833–52.
• Pei J. Is there a data science and engineering brain drain? if so, how can we rebalance them? In: Proceedings - International Conference on Data Engineering. 2019. p. 38–9.
• Gao H, Pei J, Huang H. Demystifying dropout. In: International Conference on Machine Learning. 2019. p. 2112–21.
• Chu L, Zhang Y, Wang Z, Yang Y, Pei J, Chen E. Finding theme communities from database networks. In: Proceedings of the VLDB Endowment. 2019. p. 1071–84.
• Yang W, Tan L, Lu C, Cui A, Li H, Chen X, et al. Detecting customer complaint escalation with recurrent neural networks and manually-engineered features. In: NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference. 2019. p. 56–63.
• Huang F, Gao S, Pei J, Huang H. Nonconvex zeroth-order stochastic admm methods with lower function query complexity. arXiv preprint arXiv:190713463. 2019;
• Osman AS. Data mining techniques. 2019;
• Gao H, Pei J, Huang H. Demystifying dropout. In: 36th International Conference on Machine Learning, ICML 2019. 2019. p. 3692–701.
• Wu J, Wang Y, Wang P, Pei J, Wang W. Finding Maximal Significant Linear Representation between Long Time Series. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2018. p. 1320–5.
• Zhu D, Cui P, Zhang Z, Pei J, Zhu W. High-Order Proximity Preserved Embedding for Dynamic Networks. IEEE Transactions on Knowledge and Data Engineering. 2018 Nov 1;30(11):2134–44.
• Liu J, Yang J, Xiong L, Pei J, Luo J. Skyline diagram: finding the voronoi counterpart for skyline queries. In: Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018. 2018. p. 653–64.
• Yang Y, Chu L, Zhang Y, Wang Z, Pei J, Chen E. Mining density contrast subgraphs. In: Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018. 2018. p. 221–32.
• Hu J, Pei J. Subspace multi-clustering: a review. Knowledge and Information Systems. 2018 Aug 1;56(2):257–84.
• Luo L, Zhu W, Zhang W, Zhang T, Zhang Z, Pei J. Sketched follow-the-regularized-leader for online factorization machine. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2018. p. 1900–9.
• Chu L, Hu X, Hu J, Wang L, Pei J. Exact and consistent interpretation for piecewise linear neural networks: A closed form solution. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2018. p. 1244–53.
• Zhang Z, Pei J, Cui P, Yao X, Wang X, Zhu W. Arbitrary-order proximity preserved network embedding. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2018. p. 2778–86.
• Jia Y, Pei J. Message from the general chairs: DSC 2018. In: Proceedings - 2018 IEEE 3rd International Conference on Data Science in Cyberspace, DSC 2018. 2018. p. xxv.
• Peng J, Zhang D, Wang J, Pei J. AQP++: Connecting approximate query processing with aggregate precomputation for interactive analytics. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2018. p. 1477–92.
• Lin X, Zhang W, Zhang M, Zhu W, Pei J, Zhao P, et al. Online compact convexified factorization machine. In: The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018. 2018. p. 1633–42.
• Pei J, Manolopoulos Y, Sadiq S, Li J. Database Systems for Advanced Applications: 23rd International Conference, DASFAA 2018, Gold Coast, QLD, Australia, May 21-24, 2018, Proceedings, Part II. Springer; 2018.
• Zhang Z, Cui P, Pei J, Wang X, Zhu W. Timers: Error-bounded SVD restart on dynamic networks. In: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. 2018. p. 224–31.
• Pei J, Manolopoulos Y, Sadiq S, Li J. Correction to: Database Systems for Advanced Applications. In: International Conference on Database Systems for Advanced Applications. 2018. p. C1–C1.
• Dolatshah M, Teoh M, Wang J, Pei J. Cleaning crowdsourced labels using oracles for statistical classification. In: Proceedings of the VLDB Endowment. 2018. p. 376–89.
• Dolatshah M, Teoh M, Wang J, Pei J. Cleaning Crowdsourced Labels Using Oracles For Supervised Learning. 2018.
• Amer-Yahia S, Pei J. LETTER FROM THE PROGRAM CHAIRS. Vol. 11, PROCEEDINGS OF THE VLDB ENDOWMENT. ASSOC COMPUTING MACHINERY 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA; 2018. p. VIII–VIII.
• Sadiq S, Pei J, Manolopoulos Y. Preface. Vol. 10827 LNCS. 2018.
• Sadiq S, Pei J, Manolopoulos Y. Preface. Vol. 10828 LNCS. 2018.
• Yang Y, Wang Z, Jin T, Pei J, Chen E. Tracking top-K influential vertices in dynamic networks. arXiv preprint arXiv:180301499. 2018;
• Gao C, Pei J, Wang J, Chang Y. Schemaless join for result set preferences. In: Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017. 2017. p. 569–78.
• Yang Y, Wang Z, Pei J, Chen E. Tracking Influential Individuals in Dynamic Networks. IEEE Transactions on Knowledge and Data Engineering. 2017 Nov 1;29(11):2615–28.
• Wang Z, Yang Y, Pei J, Chu L, Chen E. Activity Maximization by Effective Information Diffusion in Social Networks. IEEE Transactions on Knowledge and Data Engineering. 2017 Nov 1;29(11):2374–87.
• Moskovitch R, Wang F, Pei J, Friedman C. JASIST special issue on biomedical information retrieval. Journal of the Association for Information Science and Technology. 2017 Nov 1;68(11):2525–8.
• Yang Y, Pei J, Al-Barakati A. Measuring in-network node similarity based on neighborhoods: a unified parametric approach. Knowledge and Information Systems. 2017 Oct 1;53(1):43–70.
• Gao C, Wang J, Pei J, Li R, Chang Y. Preference-driven similarity join. In: Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017. 2017. p. 97–105.
• Kuo CY, Yeh MY, Pei J. Principal pattern mining on graphs. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. 2017. p. 278–81.
• Hu J, Qian Q, Pei J, Jin R, Zhu S. Finding multiple stable clusterings. Knowledge and Information Systems. 2017 Jun 1;51(3):991–1021.
• Liu J, Yang J, Xiong L, Pei J. Secure Skyline Queries on Cloud Platform. In: Proceedings International Conference on Data Engineering. 2017. p. 633–44.
• Yu PS, Fang B, Pei J. Message from the general chairs. In: Proceedings - 2016 IEEE 1st International Conference on Data Science in Cyberspace, DSC 2016. 2017. p. xiv.
• Campbell A, Mao X, Pei J, Al-Barakati A. Multidimensional business benchmarking analysis on data warehouses. International Journal of Data Warehousing and Mining. 2017 Jan 1;13(1):51–75.
• Choi DW, Pei J, Heinis T. Efficient mining of regional movement patterns in semantic trajectories. In: Proceedings of the VLDB Endowment. 2017. p. 2073–84.
• Campbell A, Mao X, Pei J, Al-Barakati A. Multidimensional benchmarking in data warehouses. Intelligent Data Analysis. 2017 Jan 1;21(4):781–801.
• Chu L, Wang Z, Pei J, Zhang Y, Yang Y, Chen E. Finding theme communities from database networks: from mining to indexing and query answering. arXiv preprint arXiv:170908083. 2017;
• Wang X, Cui P, Wang J, Pei J, Zhu W, Yang S. Community preserving network embedding. In: 31st AAAI Conference on Artificial Intelligence, AAAI 2017. 2017. p. 203–9.
• Liu J, Xiong L, Zhang Q, Pei J, Luo J. Eclipse: Practicability Beyond kNN and Skyline. arXiv preprint arXiv:170701223. 2017;
• Yu K, Wu X, Ding W, Pei J. Scalable and accurate online feature selection for big data. ACM Transactions on Knowledge Discovery from Data. 2016 Dec 1;11(2).
• Wang Z, Chu L, Pei J, Al-Barakati A, Chen E. Tradeoffs between density and size in extracting dense subgraphs: A unified framework. In: Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. 2016. p. 41–8.
• Vinh NX, Chan J, Romano S, Bailey J, Leckie C, Ramamohanarao K, et al. Discovering outlying aspects in large datasets. Data Mining and Knowledge Discovery. 2016 Nov 1;30(6):1520–55.
• Liu S, Yin J, Wang X, Cui W, Cao K, Pei J. Online Visual Analytics of Text Streams. IEEE transactions on visualization and computer graphics. 2016 Nov;22(11):2451–66.
• Zheng Z, Wang D, Pei J, Yuan Y, Fan C, Xiao F. Urban traffic prediction through the second use of inexpensive big data from buildings. In: International Conference on Information and Knowledge Management, Proceedings. 2016. p. 1363–72.
• Xu X, Gao C, Pei J, Wang K, Al-Barakati A. Continuous similarity search for evolving queries. Knowledge and Information Systems. 2016 Sep 1;48(3):649–78.
• Pei J, Li G, Tong H. Preface. Big Data Research. 2016 Sep 1;5:1.
• Ou M, Cui P, Pei J, Zhang Z, Zhu W. Asymmetric transitivity preserving graph embedding. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016. p. 1105–14.
• Chu L, Wang Z, Pei J, Wang J, Zhao Z, Chen E. Finding gangs in war from signed networks. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016. p. 1505–14.
• Hung HJ, Shuai HH, Yang DN, Huang LH, Lee WC, Pei J, et al. When social influence meets item inference. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016. p. 915–24.
• Pei J. Preface. Journal of Computer Science and Technology. 2016 Jul 1;31(4):635–6.
• Schuller B, Pei J. Using computer intelligence for depression diagnosis and crowdsourcing. Computer. 2016 Jul 1;49(7):8–9.
• Yang Y, Mao X, Pei J, He X. Continuous influence maximization: What discounts should we offer to social network users? In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2016. p. 727–41.
• Choi DW, Pei J, Lin X. Finding the minimum spatial keyword cover. In: 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016. 2016. p. 685–96.
• Duan L, Tang G, Pei J, Bailey J, Dong G, Nguyen V, et al. Efficient discovery of contrast subspaces for object explanation and characterization. Knowledge and Information Systems. 2016 Apr 1;47(1):99–129.
• Hu J, Qian Q, Pei J, Jin R, Zhu S. Finding multiple stable clusterings. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2016. p. 171–80.
• Han J, Kamber M, Pei J, Data Mining MK, Hastie T, Tibshirani R, et al. Bachelor-und Masterarbeitsthemen im WS 2016/2017. 2016;
• Pei J, Akoglu L, Lee H, Levandoski J, Li X, Meo R, et al. EIC Editorial. IEEE Transactions on Knowledge & Data Engineering. 2016;28:2535–7.
• Duan L, Tang G, Pei J, Bailey J, Campbell A, Tang C. Mining outlying aspects on numeric data. Data Mining and Knowledge Discovery. 2015 Sep 22;29(5):1116–51.
• Tang G, Pei J, Bailey J, Dong G. Mining multidimensional contextual outliers from categorical relational data. Intelligent Data Analysis. 2015 Sep 8;19(5):1171–92.
• Pei J, Silvestri F, Tang J. Welcome from the ASONAM 2015 program chairs. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. 2015. p. xxii.
• Zhang Y, Tang J, Yang Z, Pei J, Yu PS. COSNET: Connecting heterogeneous social networks with local and global consistency. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015. p. 1485–94.
• Yu K, Wang D, Pei J, Ding W, Small DL, Islam S, et al. Tornado forecasting with multiple Markov boundaries. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015. p. 2237–46.
• Zhang X, Dou W, Pei J, Nepal S, Yang C, Liu C, et al. Proximity-aware local-recoding anonymization with MapReduce for scalable big data privacy preservation in cloud. IEEE Transactions on Computers. 2015 Aug 1;64(8):2293–307.
• Pei J. Preface. Journal of Computer Science and Technology. 2015 Jul 1;30(4):655–6.
• Yu K, Ding W, Simovici DA, Wang H, Pei J, Wu X. Classification with streaming features: An emerging-pattern mining approach. ACM Transactions on Knowledge Discovery from Data. 2015 Jun 1;9(4):1–31.
• Wang J, Song S, Lin X, Zhu X, Pei J. Cleaning structured event logs: A graph repair approach. In: Proceedings - International Conference on Data Engineering. 2015. p. 30–41.
• Yang Y, Lu Q, Tang G, Pei J. The impact of market competition on search advertising. Journal of Interactive Marketing. 2015 May 1;30:46–55.
• Pei J, Fan J. Message from the conference chairs. In: IEEE International Conference on Data Mining Workshops, ICDMW. 2015. p. xvii–xviii.
• Liu J, Xiong L, Pei J, Luo J, Zhang H. Finding pareto optimal groups: Group-based skyline. In: Proceedings of the VLDB Endowment. 2015. p. 2086–97.
• Pei J, Silvestri F, Tang J. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015. ACM; 2015.
• Vinh NX, Chan J, Bailey J, Leckie C, Ramamohanarao K, Pei J. Scalable outlying-inlying aspects discovery via feature ranking. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015. p. 422–34.
• Chen J, Zhang R, Khan SU, Parashar M, Pei J, Zomaya A, et al. Message from the DSDIS2015 Chairs. In: Proceedings - 2015 IEEE International Conference on Data Science and Data Intensive Systems; 8th IEEE International Conference Cyber, Physical and Social Computing; 11th IEEE International Conference on Green Computing and Communications and 8th IEEE International Conference on Internet of Things, DSDIS/CPSCom/GreenCom/iThings 2015. 2015. p. xiv–xv.
• Yu Z, Yu X, Liu Y, Li W, Pei J. Mining frequent co-occurrence patterns across multiple data streams. In: EDBT 2015 - 18th International Conference on Extending Database Technology, Proceedings. 2015. p. 73–84.
• Lin YF, Chen HH, Tseng VS, Pei J. Reliable early classification on multivariate time series with numerical and categorical attributes. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015. p. 199–211.
• Chang L, Lin X, Qin L, Yu JX, Pei J. Efficiently computing Top-K shortest path join. In: EDBT 2015 - 18th International Conference on Extending Database Technology, Proceedings. 2015. p. 133–44.
• Chu L, Wang S, Liu S, Huang Q, Pei J. ALID: Scalable dominant cluster detection. In: Proceedings of the VLDB Endowment. 2015. p. 826–37.
• Pei J. State of the Journal Editorial. IEEE Transactions on Knowledge & Data Engineering. 2015;27:1–2.
• Yang Y, Pei J. In-network neighborhood-based node similarity measure: A unified parametric model. arXiv preprint arXiv:151003814. 2015;
• Han J, Wen JR, Pei J. Within-network classification using radius-constrained neighborhood patterns. In: CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. 2014. p. 1539–48.
• Tang G, Wu K, Pei J, Tang J, Lei J. An appliance-driven approach to detection of corrupted load curve data. In: CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. 2014. p. 1429–38.
• Huang D, Xu K, Pei J. Malicious URL detection by dynamically mining patterns without pre-defined elements. World Wide Web. 2014 Nov 1;17(6):1375–94.
• Zhang L, Pei J, Jia Y, Zhou B, Wang X. Do neighbor buddies make a difference in reblog likelihood? An analysis on SINA Weibo data. In: ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 2014. p. 208–15.
• Tang G, Pei J, Luk WS. Email mining: Tasks, common techniques, and tools. Knowledge and Information Systems. 2014 Oct 1;41(1):1–31.
• Han J, Pei J. Pattern-growth methods. In: Frequent Pattern Mining. 2014. p. 65–81.
• Pei J. EIC editorial. IEEE Transactions on Knowledge and Data Engineering. 2014 Jul 1;26(7):1559–61.
• Yang Y, Yu JX, Gao H, Pei J, Li J. Mining most frequently changing component in evolving graphs. World Wide Web. 2014 May 1;17(3):351–76.
• Yang C, Zhang X, Zhong C, Liu C, Pei J, Ramamohanarao K, et al. A spatiotemporal compression based approach for efficient big data processing on Cloud. Journal of Computer and System Sciences. 2014 Jan 1;80(8):1563–83.
• Pei J. Editorial. IEEE Transactions on Knowledge and Data Engineering. 2014 Jan 1;26(1):1–2.
• Guo T, Zhu X, Pei J, Zhang C. SNOC: Streaming Network Node Classification. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2014. p. 150–9.
• Chan J, Vinh NX, Liu W, Bailey J, Leckie CA, Ramamohanarao K, et al. Structure-aware distance measures for comparing clusterings in graphs. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2014. p. 362–73.
• Li Y, Bailey J, Kulik L, Pei J. Efficient matching of substrings in uncertain sequences. In: SIAM International Conference on Data Mining 2014, SDM 2014. 2014. p. 767–75.
• Qian Q, Hu J, Jin R, Pei J, Zhu S. Distance metric learning using dropout: A structured regularization approach. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2014. p. 323–32.
• Srihari S, Setlur S, Pei J, others. Epidemiological Analysis and Early Warning of Disease Outbreaks by Automated Reading and Mining of Pre-Hospital Care Reports. 2014;
• Wang Y, Pei J, Lin X, Zhang Q, Zhang W. An iterative fusion approach to graph-based semi-supervised learning from multiple views. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2014. p. 162–73.
• Chen WG, Chen XL, Hu SM, Pei J, Xie T, Zhu WW. Editorial: Moving forward to respond to rapid changes of computer science and technology. Journal of Computer Science and Technology. 2014 Jan 1;29(1):1.
• Yu K, Wu X, Ding W, Pei J. Towards Scalable and Accurate Online Feature Selection for Big Data. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2014. p. 660–9.
• Pei J, Fan J. Message from the Conference Chairs. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2014. p. xv–xvi.
• Hu J, Pei J, Tang J. How can I index my thousands of photos effectively and automatically? An unsupervised feature selection approach. In: SIAM International Conference on Data Mining 2014, SDM 2014. 2014. p. 136–44.
• Hu X, Pei J, Tao Y. Shortest unique queries on strings. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2014. p. 161–72.
• Duan L, Tang G, Pei J, Bailey J, Dong G, Campbell A, et al. Mining contrast subspaces. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2014. p. 249–60.
• Pei J. Managing Data-Intensive Applications in the Cloud. Computer. 2014;47:6–6.
• Pei J, others. A Semantic Summarization Approach to Data Warehousing and Online Analytical Processing. 2014;
• Zhang Y, Zhang W, Pei J, Lin X, Lin Q, Li A. Consensus-based ranking of multivalued objects: A generalized borda count approach. IEEE Transactions on Knowledge and Data Engineering. 2014 Jan 1;26(1):83–96.
• Kumar R, Toivonen H, Pei J, Huang JZ, Wu X. Proceedings, 14th IEEE International Conference on Data Mining: 14–17 December 2014 Shenzhen, China. 2014;
• Hu X, Pei J, Tao Y. Shortest Unique Queries on Strings. In Springer International Publishing; 2014. p. 161–72.
• Nejdl W, Pei J, Rastogi R, Silvestri F. Program committee chairs' welcome message. In: International Conference on Information and Knowledge Management, Proceedings. 2013.
• Li Y, Bailey J, Kulik L, Pei J. Mining probabilistic frequent spatio-temporal sequential patterns with gap constraints from uncertain databases. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2013. p. 448–57.
• Lo YC, Li JY, Yeh MY, Lin SD, Pei J. What distinguish one from its peers in social networks? In: Data Mining and Knowledge Discovery. 2013. p. 396–420.
• Xiong Y, Zhu Y, Yu PS, Pei J. Towards cohesive anomaly mining. In: Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013. 2013. p. 984–90.
• Tang G, Yang Y, Pei J. Price information patterns in web search advertising: An empirical case study on accommodation industry. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2013. p. 737–46.
• Low-Kam C, Raïssi C, Kaytoue M, Pei J. Mining statistically significant sequential patterns. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2013. p. 488–97.
• Pei J. Some new progress in analyzing and mining uncertain and probabilistic data for big data analytics. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2013. p. 38–45.
• Mao X, Lin B, Cai D, He X, Pei J. Parallel field alignment for cross media retrieval. In: MM 2013 - Proceedings of the 2013 ACM Multimedia Conference. 2013. p. 897–906.
• Jiang D, Pei J, Li H. Mining search and browse logs for web search: A survey. ACM Transactions on Intelligent Systems and Technology. 2013 Oct 21;4(4).
• Liao Z, Jiang D, Pei J, Huang Y, Chen E, Cao H, et al. A vlHMM approach to context-aware search. ACM Transactions on the Web. 2013 Oct 1;7(4).
• Tang G, Pei J, Bailey J, Dong G. Mining multidimensional contextual outliers from categorical relational data. In: ACM International Conference Proceeding Series. 2013.
• Pei J, Wu WCH, Yeh MY. On shortest unique substring queries. In: Proceedings - International Conference on Data Engineering. 2013. p. 937–48.
• Jiang B, Pei J, Tao Y, Lin X. Clustering uncertain data based on probability distribution similarity. IEEE Transactions on Knowledge and Data Engineering. 2013 Mar 11;25(4):751–63.
• Cui Y, Pei J, Tang G, Luk WS, Jiang D, Hua M. Finding email correspondents in online social networks. World Wide Web. 2013 Mar 1;16(2):195–218.
• Huang J, Jiang B, Pei J, Chen J, Tang Y. Skyline distance: A measure of multidimensional competence. Knowledge and Information Systems. 2013 Feb 1;34(2):373–96.
• Chen J, Huang J, Jiang B, Pei J, Yin J. Recommendations for two-way selections using skyline view queries. Knowledge and Information Systems. 2013 Feb 1;34(2):397–424.
• Pei J. New EIC editorial. IEEE Transactions on Knowledge and Data Engineering. 2013 Jan 2;25(2):245.
• Pei J. Editorial [State of the Transactions]. IEEE Transactions on Knowledge and Data Engineering. 2013;26:1–2.
• Li J, Cao L, Lim E-P, Zhou Z-H, Ho T-B, Cheung D, et al. Trends and Applications in Knowledge Discovery and Data Mining. Springer doi. 2013;10:978–3.
• Pei J. Editorial. IEEE Transactions on Knowledge and Data Engineering. 2013 Jan 1;25(8):1689–92.
• Chen J, Nepal S, Cafaro M, Pei J, Sun XH, Lin X, et al. Message from BDSE2013 Chairs. In: Proceedings - 16th IEEE International Conference on Computational Science and Engineering, CSE 2013. 2013. p. 30.
• Li J, Cao L, Wang C, Tan KC, Liu B, Pei J, et al. Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2013 Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Golden Coast, QLD, Australia, Revised Selected Papers. Springer; 2013.
• Pei J. Editorial [2012 & 2013 Associate Editors]. IEEE Transactions on Knowledge and Data Engineering. 2013;25:1689–92.
• Yu W, Lin X, Zhang W, Chang L, Pei J. More is simpler: Effectively and efficiently assessing nodepair similarities based on hyperlinks. In: Proceedings of the VLDB Endowment. 2013. p. 13–24.
• Li J, Zhang K, Pei J, Liu L, Chan L, Geng Z, et al. Preface to the first IEEE ICDM workshop on causal discovery. In: Proceedings - IEEE 13th International Conference on Data Mining Workshops, ICDMW 2013. 2013.
• Tang G, Wu K, Pei J, Tang J, Lei J. Household Electricity Consumption Data Cleansing. arXiv preprint arXiv:13077757. 2013;
• Ye N. Association rules. In: Data Mining. 2013. p. 185–95.
• Tang G, Wu K, Pei J, Tang J, Lei J. Is My Electricity Bill Accurate? A Model-Driven Approach to Corrupted Load Data Identification. CoRR. 2013;
• Wang Y, Wang P, Pei J, Wang W, Huang S. A data-adaptive and dynamic segmentation index for whole matching on time series. Proceedings of the VLDB Endowment. 2013 Jan 1;6(10):793–804.
• Agarwal D, Caruana R, Pei J, Wang K. Introduction to the Special Issue ACM SIGKDD 2012. Vol. 7, ACM Transactions on Knowledge Discovery from Data (TKDD). ACM New York, NY, USA; 2013. p. 1–2.
• Liu W, Kan A, Chan J, Bailey J, Leckie C, Pei J, et al. On compressing weighted time-evolving graphs. In: ACM International Conference Proceeding Series. 2012. p. 2319–22.
• Dwyer T, Fedorova A, Blagodurov S, Roth M, Gaud F, Pei J. A practical method for estimating performance degradation on multicore processors, and its application to HPC workloads. In: International Conference for High Performance Computing, Networking, Storage and Analysis, SC. 2012.
• Maserrat H, Pei J. Community preserving lossy compression of social networks. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2012. p. 509–18.
• Liu Y, Zhao Y, Chen L, Pei J, Han J. Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays. IEEE Transactions on Parallel and Distributed Systems. 2012 Oct 16;23(11):2138–49.
• Li L, Petschulat S, Tang G, Pei J, Luk WS. Efficient and effective aggregate keyword search on relational databases. International Journal of Data Warehousing and Mining. 2012 Oct 1;8(4):41–81.
• Hu Y, Qian Y, Li H, Jiang D, Pei J, Zheng Q. Mining query subtopics from search log data. In: SIGIR’12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. 2012. p. 305–14.
• Hua M, Pei J. Clustering in applications with multiple data sources-A mutual subspace clustering approach. Neurocomputing. 2012 Sep 1;92:133–44.
• Abiteboul S, Koch C, Tan KL, Pei J. Guest editors' Introduction to the special section on the 27th international conference on data engineering (ICDE 2011). IEEE Transactions on Knowledge and Data Engineering. 2012 Aug 30;24(10):1729–30.
• Wu WCH, Yeh MY, Pei J. Random error reduction in similarity search on time series: A statistical approach. In: Proceedings - International Conference on Data Engineering. 2012. p. 858–69.
• Hua M, Pei J. Aggregate queries on probabilistic record linkages. In: ACM International Conference Proceeding Series. 2012. p. 360–71.
• Xing Z, Pei J, Yu PS. Early classification on time series. Knowledge and Information Systems. 2012 Apr 1;31(1):105–27.
• Melli G, Wu X, Beinat P, Bonchi F, Cao L, Duan R, et al. Top-10 data mining case studies. International Journal of Information Technology and Decision Making. 2012 Mar 1;11(2):389–400.
• Zhou B, Pei J. Aggregate keyword search on large relational databases. Knowledge and Information Systems. 2012 Feb 1;30(2):283–318.
• Jiang B, Pei J, Lin X, Yuan Y. Probabilistic skylines on uncertain data: Model and bounding-pruning- refining methods. Journal of Intelligent Information Systems. 2012 Feb 1;38(1):1–39.
• Han J, Kamber M, Pei J. Data mining: concepts and techniques, Waltham, MA. Morgan Kaufman Publishers. 2012;10:978–1.
• Han J, Kamber M, Pei J. Data Cube Technology. In: Data Mining. 2012. p. 187–242.
• Han J, Kamber M, Pei J. Data mining: Data mining concepts and techniques. Data Min Concepts Tech. 2012;3:740–740.
• Yang Q, Agarwal D, Pei J. Proceedings of the Data Mining and Intelligent Knowledge Management Workshop. 2012;
• Jiang Q, Campbell A, Tang G, Pei J. Multi-level relationship outlier detection. International Journal of Business Intelligence and Data Mining. 2012 Jan 1;7(4):253–73.
• Jiawei H, Kamber M, Han J, Pei J. Data Mining: Concepts and Techniques [Internet]. San Francisco, CA, itd. Morgan Kaufmann; 2012.
• Han JW, Kamber M, Pei J. Clustering analysis. Data Mining: Concept and Technique, MK imprint of Elsevier, New York. 2012;478–90.
• Kamber JHM, Han JPJ, Kamber M, Pei J. Outlier Detection. 2012;
• Mclachlan G, Pei J, Faloutsos C, Melli G, Bonchi F, Ghani R, et al. Top-10 Data Mining Case Studies. 2012;
• Fan W. Web-age information management. Springer, Berlin; 2012.
• Han J, Kamber M, Pei J. Classification: advanced methods. Data mining concepts and techniques. 2012;393–443.
• Han J, Kamber M, Pei J. Data warehousing and online analytical processing. In: Data Mining. 2012. p. 125–85.
• Pei J. MSC BD 5002/IT 5210: Knowledge Discovery and Data Mining. 2012;
• Han J, Kamber M, Pei J. Classification: basic concepts. In: Data Mining. 2012. p. 327–91.
• Pei J, Barnum P. COMP5331: Knowledge Discovery and Data Mining. 2012;
• Han J, Kamber M, Pei J. Outlier detection. Data mining: concepts and techniques. 2012;543–84.
• Han J, Kamber M, Pei J. 6-mining frequent patterns, associations, and correlations: Basic concepts and methods. Data mining: concepts and techniques. 2012;243–78.
• Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques. 2012.
• Han J, Kamber M, Pei J, others. Getting to know your data. In: Data mining. 2012. p. 39–82.
• Sun X, Wang H, Li J, Pei J. Publishing anonymous survey rating data. Data Mining and Knowledge Discovery. 2011 Nov 1;23(3):379–406.
• Liao Z, Jiang D, Chen E, Pei J, Cao H, Li H. Mining concept sequences from large-scale search logs for context-aware query suggestion. ACM Transactions on Intelligent Systems and Technology. 2011 Oct 1;3(1).
• Wong RCW, Fu AWC, Wang K, Yu PS, Pei J. Can the utility of anonymized data be used for privacy breaches? ACM Transactions on Knowledge Discovery from Data. 2011 Aug 1;5(3).
• Zhang Y, Zhang W, Lin X, Jiang B, Pei J. Ranking uncertain sky: The probabilistic top-k skyline operator. Information Systems. 2011 Jul 1;36(5):898–915.
• Jiang B, Pei J. Outlier detection on uncertain data: Objects, instances, and inferences. In: Proceedings - International Conference on Data Engineering. 2011. p. 422–33.
• He Q, Kifer D, Pei J, Mitra P, Lee Giles C. Citation recommendation without author supervision. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining, WSDM 2011. 2011. p. 755–64.
• Kang D, Jiang D, Pei J, Liao Z, Sun X, Choi HJ. Multidimensional mining of large-scale search logs: A topic-concept cube approach. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining, WSDM 2011. 2011. p. 385–94.
• Hua M, Pei J, Lin X. Ranking queries on uncertain data. VLDB Journal. 2011 Feb 1;20(1):129–53.
• Hua M, Pei J. Continuous Ranking Queries on Uncertain Streams. In: Ranking Queries on Uncertain Data. Springer, New York, NY; 2011. p. 129–50.
• Hay M, Liu K, Miklau G, Pei J, Terzi E. Privacy-aware data management in information networks. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2011. p. 1201–4.
• Tao Y, Sheng C, Pei J. On k-skip shortest paths. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2011. p. 421–32.
• Xing Z, Pei J, Yu PS, Wang K. Extracting interpretable features for early classification on time series. In: Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011. 2011. p. 247–58.
• Raïssi C, Pei J. Towards bounding sequential patterns. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011. p. 1379–87.
• Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques [Internet]. Waltham. Elsevier; 2011.
• Wang C, Yuan LY, You JH, Zaiane OR, Pei J. On pruning for top-k ranking in uncertain databases. In: Proceedings of the VLDB Endowment. 2011. p. 598–609.
• Jiawei Han MK, Pei J. Data mining: concepts and techniques: concepts and techniques. Elsevier, Amsterdam; 2011.
• Pei J, Gama J, Yang Q, Huang R, Li X. Best papers from the Fifth International Conference on Advanced Data Mining and Applications (ADMA 2009). Knowledge and Information Systems. 2011 Jan 1;27(2):163–4.
• Hua M, Pei J. Top-k Typicality Queries on Uncertain Data. In: Ranking Queries on Uncertain Data. Springer, New York, NY; 2011. p. 51–87.
• Pei J. Early Classification: Problems. Preliminary Results, and Opportunities. 2011;
• Zhou B, Pei J. The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks. Knowledge and Information Systems. 2011 Jan 1;28(1):47–77.
• Hua M, Pei J. Ranking Queries on Probabilistic Linkages. In: Ranking Queries on Uncertain Data. Springer, New York, NY; 2011. p. 151–84.
• Hua M, Pei J, Lin X. Ranking queries on uncertain data. The VLDB Journal. 2011;20:129–53.
• Jiang D, Pei J, Li H. Enhancing web search by mining search and browse logs. In: SIGIR. 2011. p. 1295–6.
• Hua M, Pei J. Probabilistic Ranking Queries on Uncertain Data. In: Ranking Queries on Uncertain Data. Springer, New York, NY; 2011. p. 89–128.
• Liu Y, Zhao Y, Chen L, Pei J, Han J. Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays. IEEE Transactions on Parallel and Distributed Systems. 2011;23:2138–49.
• Pei J. Special Issue: Best Papers from the Fifth International Conference on Advanced Data Mining and Applications (ADMA 2009). Springer; 2011.
• Shen HT, Pei J. WAIM 2010 Workshop chair's message. Vol. 6185 LNCS. 2010.
• Wong RCW, Fu AWC, Wang K, Xu Y, Pei J, Yu PS. Probabilistic inference protection on anonymized data. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2010. p. 1127–32.
• Maserrat H, Pei J. Neighbor query friendly compression of social networks. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2010. p. 533–41.
• Xiang B, Jiang D, Pei J, Sun X, Chen E, Li H. Context-aware ranking in web search. In: SIGIR 2010 Proceedings - 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2010. p. 451–8.
• Tsoukalas KJ, Pei J, Cubranic D. Systems and methods to automatically generate enhanced information associated with a selected web table. 2010.
• Tao Y, Yi K, Sheng C, Pei J, Li F. Logging every footstep: Quantile summaries for the entire history. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2010. p. 639–50.
• He Q, Pei J, Kifer D, Mitra P, Giles L. Context-aware citation recommendation. In: Proceedings of the 19th International Conference on World Wide Web, WWW ’10. 2010. p. 421–30.
• Jiang D, Pei J, Li H. Web search/browse log mining: Challenges, methods, and applications. In: Proceedings of the 19th International Conference on World Wide Web, WWW ’10. 2010. p. 1351–2.
• Cheng X, Xu J, Pei J, Liu J. Hierarchical distributed data classification in wireless sensor networks. In: Computer Communications. 2010. p. 1404–13.
• Lin Z, Jiang B, Pei J, Jiang D. Mining discriminative items in multiple data streams. World Wide Web. 2010 Jul 12;13(4):497–522.
• Xing Z, Pei J. Exploring disease association from the NHANES data: Data mining, pattern summarization, and visual analytics. International Journal of Data Warehousing and Mining. 2010 Jul 1;6(3):11–27.
• Yuen SM, Tao Y, Xiao X, Pei J, Zhang D. Superseding nearest neighbor search on uncertain spatial databases. IEEE Transactions on Knowledge and Data Engineering. 2010 Jun 4;22(7):1041–55.
• Hua M, Pei J. Probabilistic path queries in road networks: Traffic uncertainty aware path selection. In: Advances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings. 2010. p. 347–58.
• Huang J, Zhao F, Chen J, Pei J, Yin J. Towards progressive and load balancing distributed computation: A case study on skyline analysis. Journal of Computer Science and Technology. 2010 May 1;25(3):431–43.
• Cheema MA, Lin X, Wang W, Zhang W, Pei J. Probabilistic reverse nearest neighbor queries on uncertain data. IEEE Transactions on Knowledge and Data Engineering. 2010 Apr 1;22(4):550–64.
• Aljaber B, Stokes N, Bailey J, Pei J. Document clustering of scientific texts using citation contexts. Information Retrieval. 2010 Apr 1;13(2):101–31.
• Loekito E, Bailey J, Pei J. A binary decision diagram based approach for mining frequent subsequences. Knowledge and Information Systems. 2010 Jan 1;24(2):235–68.
• Goethals B, Pei J. Special issue on the best papers of SDM'10. Statistical Analysis and Data Mining. 2010 Jan 1;3(6):359–60.
• Raïssi C, Pei J, Kister T. Computing closed skycubes. Proceedings of the VLDB Endowment. 2010 Jan 1;3(1):838–47.
• Zhang W, Lin X, Zhang Y, Pei J, Wang W. Threshold-based probabilistic top-k dominating queries. VLDB Journal. 2010 Jan 1;19(2):283–305.
• Tao Y, Pei J, Li J, Xiao K, Yi K, Xing Z. Correlation hiding by independence masking. In: Proceedings - International Conference on Data Engineering. 2010. p. 964–7.
• Parthasarathy S, Liu B, Goethals B, Pei J, Kamath C. 10th SIAM International Conference on Data Mining 2010. 2010;
• Pei J, Getoor L, de Keijzer A. Summary of the first ACM SIGKDD workshop on knowledge discovery from uncertain data (U’09). ACM SIGKDD Explorations Newsletter. 2010;11:90–1.
• Shen HT, Pei J, Özsu MT, Zou L, Lu J, Ling TW, et al. Web-Age Information Management. WAIM 2010 Workshops: WAIM 2010 International Workshops: IWGD 2010, WCMT 2010, XMLDM 2010, Jiuzhaigou Valley, China, July 15-17, 2010, Revised Selected Papers. Springer Science & Business Media; 2010.
• Xing Z, Pei J, Keogh E. A brief survey on sequence classification. ACM Sigkdd Explorations Newsletter. 2010;12:40–8.
• Parthasarathy S, Liu B, Goethals B, Pei J, Kamath C. Proceedings of the 2010 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics; 2010.
• Arun R, Suresh V, Madhavan CEV, Murthy MNN, Zaki MJ, Yu JX, et al. Advances in knowledge discovery and data mining. Berlin Heidelberg: Springer; 2010.
• Pei J. Outlier Detection. 2010;
• Jiang D, Pei J, Li H. Search and browse log mining for web information retrieval: challenges, methods, and applications. In: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. 2010. p. 912–912.
• Shen HT, Pei J, Özsu MT, Zou L, Lu J, Ling TW, et al. Web-Age Information Management. WAIM 2010 Workshops: WAIM 2010 International Workshops: IWGD 2010, WCMT 2010, XMLDM 2010, Jiuzhaigou Valley, China, July 15-17, 2010, Revised Selected Papers. Springer Science & Business Media; 2010.
• Wang T, Yang B, Gao J, Yang D, Tang S, Wu H, et al. MobileMiner: A real world case study of data mining in mobile communication. In: SIGMOD-PODS’09 - Proceedings of the International Conference on Management of Data and 28th Symposium on Principles of Database Systems. 2009. p. 1083–5.
• Han Y, Zhou B, Pei J, Jia Y. Understanding importance of collaborations in co-authorship networks: A supportiveness analysis approach. In: Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics. 2009. p. 1105–16.
• Cao H, Jiang D, Pei J, Chen E, Li H. Towards context-aware search by learning a very large variable length Hidden Markov Model from search logs. In: WWW’09 - Proceedings of the 18th International World Wide Web Conference. 2009. p. 191–200.
• He Q, Chen B, Pei J, Qiu B, Mitra P, Giles L. Detecting topic evolution in scientific literature: How can citations help? In: International Conference on Information and Knowledge Management, Proceedings. 2009. p. 957–66.
• Pei J, Zhang X, Cho M, Wang H, Yu PS. On mining maximal pattern-based clusters. In: Data Mining for Business Applications. 2009. p. 31–52.
• Wang J, He X, Wang C, Pei J, Bu J, Chen C, et al. News article extraction with template-independent wrapper. In: WWW’09 - Proceedings of the 18th International World Wide Web Conference. 2009. p. 1085–6.
• Pei J, Getoor L, De Keijzer A. Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data, U'09 in Conjunction with KDD'09: Forward. In: Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data, U’09 in Conjunction with KDD’09. 2009.
• Wang J, Chen C, Wang C, Pei J, Bu J, Guan Z, et al. Can we learn a template-independent wrapper for news article extraction from a single training site? In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2009. p. 1345–53.
• Zhou B, Jiang D, Pei J, Li H. OLAP on search logs: An infrastructure supporting data-driven applications in search engines. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2009. p. 1395–403.
• Qiang Y, Ronghuai H, Jian P, Gama J, Xiaofeng M. Advanced Data Mining and Applications: 5th International Conference, ADMA 2009 Beijing, China, August 17-19, 2009 Proceedings - Preface. Vol. 5678 LNAI. 2009.
• Zhao Y, Zhang H, Wu S, Pei J, Cao L, Zhang C, et al. Debt detection in social security by sequence classification using both positive and negative patterns. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2009. p. 648–63.
• Xiac Y, Wu W, Pei J, Wang W, He Z. Efficiently indexing shortest paths by exploiting symmetry in graphs. In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, EDBT’09. 2009. p. 493–504.
• Zhou B, Pei J. Answering aggregate keyword queries on relational databases using minimal group-bys. In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, EDBT’09. 2009. p. 108–19.
• Tsoukalas K, Zhou B, Pei J, Cubranic D. Personalizing entity detection and recommendation with a fusion of web log mining techniques. In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, EDBT’09. 2009. p. 1100–3.
• Zhong H, Xie T, Zhang L, Pei J, Mei H. MAPO: mining and recommending api usage patterns. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2009. p. 318–43.
• Hua M, Pei J. Continuously monitoring top-k uncertain data streams: a probabilistic threshold method. Distributed and Parallel Databases. 2009 Aug 1;26(1):29–65.
• Li Q, Feng L, Pei J, Wang SX, Zhou X, Zhu Q. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface. Vol. 5446. 2009.
• Tao Y, Ding L, Lin X, Pei J. Distance-based representative skyline. In: Proceedings - International Conference on Data Engineering. 2009. p. 892–903.
• Jiang B, Pei J. Online interval skyline queries on time series. In: Proceedings - International Conference on Data Engineering. 2009. p. 1036–47.
• Zhou B, Pei J. Link spam target detection using page farms. ACM Transactions on Knowledge Discovery from Data. 2009 Jul 1;3(3).
• Hua M, Pei J, Fu AWC, Lin X, Leung HF. Top-k typicality queries and efficient query answering methods on large databases. VLDB Journal. 2009 Jun 1;18(3):809–35.
• Wong RCW, Fu AWC, Wang K, Pei J. Anonymization-based attacks in privacy-preserving data publishing. ACM Transactions on Database Systems. 2009 Jun 1;34(2).
• Ng M-P, Vergara IA, Frech C, Chen Q, Zeng X, Pei J, et al. OrthoClusterDB: an online platform for synteny blocks. BMC bioinformatics. 2009 Jun;10:192.
• Chi-Wing Wong R, Wai-Chee Fu A, Wang K, Xu Y, Pei J, Yu PS. Anonymization with Worst-Case Distribution-Based Background Knowledge. arXiv e-prints. 2009;arXiv-0909.
• Zhou B, Han Y, Pei J, Jiang B, Tao Y, Jia Y. Continuous privacy preserving publishing of data streams. In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, EDBT’09. 2009. p. 648–59.
• Wong RCW, Pei J, Fu AWC, Wang K. Online skyline analysis with dynamic preferences on nominal attributes. IEEE Transactions on Knowledge and Data Engineering. 2009 Jan 1;21(1):35–49.
• Pei J, Tao Y, Li J, Xiao X. Privacy preserving publishing on multiple quasi-identifiers. In: Proceedings - International Conference on Data Engineering. 2009. p. 1132–5.
• Jiang D, Pei J. Mining frequent cross-graph quasi-cliques. ACM Transactions on Knowledge Discovery from Data. 2009 Jan 1;2(4).
• Zeng X, Pei J, Wang K, Li J. PADS: A simple yet effective pattern-aware dynamic search method for fast maximal frequent pattern mining. Knowledge and Information Systems. 2009 Jan 1;20(3):375–91.
• Xing Z, Pei J, Yu PS. Early prediction on time series: A nearest neighbor approach. In: IJCAI International Joint Conference on Artificial Intelligence. 2009. p. 1297–302.
• Hua M, Lau MK, Pei J, Wu K. Continuous K-means monitoring with low reporting cost in sensor networks. IEEE Transactions on Knowledge and Data Engineering. 2009 Jan 1;21(12):1679–91.
• Pei J. Towards web search engine scale data mining. In: Proceedings of the Eighth Australasian Data Mining Conference-Volume 101. 2009. p. 5–5.
• Li Q, Feng L, Pei J, Wang XS, Zhou X, Zhu Q. Advances in Data and Web Management: Joint International Conferences, APWeb/WAIM 2009, Suzhou, China, April 2-4, 2009, Proceedings. Springer; 2009.
• Pei J, Getoor L, de Keijzer A. Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data. ACM; 2009.
• Pei J, Tao Y, Li J, Xiao X. Privacy preserving publishing on multiple quasi-identifiers. In: 2009 IEEE 25th International Conference on Data Engineering. 2009. p. 1132–5.
• She R, Chu JS-C, Wang K, Pei J, Chen N. GenBlastA: enabling BLAST to identify homologous gene sequences. Genome research. 2009 Jan;19(1):143–9.
• Wong RC-W, Fu AW-C, Wang K, Xu Y, Pei J, Yu PS. Anonymization with Worst-Case Distribution-Based Background Knowledge. arXiv preprint arXiv:09091127. 2009;
• Ng M-P, Vergara IA, Frech C, Chen Q, Zeng X, Pei J, et al. OrthoClusterDB: an online platform for synteny blocks. BMC bioinformatics. 2009;10:1–8.
• Huang H-C. Information hiding and applications. Springer; 2009.
• Han Y, Zhou B, Pei J, Jia Y. Understanding importance of collaborations in co-authorship networks: A supportiveness analysis approach. In: Proceedings of the 2009 SIAM International Conference on Data Mining. 2009. p. 1112–23.
• Zhao Y, Zhang H, Wu S, Pei J, Cao L, Zhang C, et al. Debt detection in social security by sequence classification using both positive and negative patterns. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 2009. p. 648–63.
• Zhou B, Pei J. OSD: An online web spam detection system. In: In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD. 2009.
• Soroush E, Wu K, Pei J. Fast and quality-guaranteed data streaming in resource-constrained sensor networks. In: Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc). 2008. p. 391–400.
• Hua M, Pei J. DiMaC: A system for cleaning disguised missing data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2008. p. 1263–6.
• Hua M, Pei J, Zhang W, Lin X. Ranking queries on uncertain data: A probabilistic threshold approach. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2008. p. 673–86.
• Cao H, Jiang D, Pei J, He Q, Liao Z, Chen E, et al. Context-aware query suggestion by mining click-through and session data. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008. p. 875–83.
• Jiang B, Pei J, Lin X, Cheung DW, Han J. Mining preferences from superior and inferior examples. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008. p. 390–8.
• Xu Y, Fung BCM, Wang K, Fu AWC, Pei J. Publishing sensitive transactions for itemset utility. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2008. p. 1109–14.
• Hua M, Pei J. DiMaC: A disguised missing data cleaning tool. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008. p. 1077–80.
• Hua M, Pei J, Zhang W, Lin X. Efficiently answering probabilistic threshold top-k queries on uncertain data. In: Proceedings - International Conference on Data Engineering. 2008. p. 1403–5.
• Zhou B, Pei J. Preserving privacy in social networks against neighborhood attacks. In: Proceedings - International Conference on Data Engineering. 2008. p. 506–15.
• Tsoukalas K, Zhou B, Pei J, Cubranic D. PLEDS: A personalized entity detection system based on web log mining techniques. In: Proceedings - The 9th International Conference on Web-Age Information Management, WAIM 2008. 2008. p. 389–96.
• Zhang W, Lin X, Pei J, Zhang Y. Managing uncertain data: Probabilistic approaches. In: Proceedings - The 9th International Conference on Web-Age Information Management, WAIM 2008. 2008. p. 405–12.
• Li J, Wong RCW, Fu AWC, Pei J. Anonymization by local recoding in data with attribute hierarchical taxonomies. IEEE Transactions on Knowledge and Data Engineering. 2008 Sep 1;20(9):1181–94.
• Wang H, Pei J. Clustering by pattern similarity. Journal of Computer Science and Technology. 2008 Jul 1;23(4):481–96.
• Fung BCM, Wang K, Fu AWC, Pei J. Anonymity for continuous data publishing. In: Advances in Database Technology - EDBT 2008 - 11th International Conference on Extending Database Technology, Proceedings. 2008. p. 264–75.
• Zeng X, Pei J, Vergara IA, Nesbitt MJ, Wang K, Chen N. OrthoCluster: A new tool for mining synteny blocks and applications in comparative genomics. In: Advances in Database Technology - EDBT 2008 - 11th International Conference on Extending Database Technology, Proceedings. 2008. p. 656–67.
• Xing Z, Dong G, Pei J, Yu PS. Mining sequence classifiers for early prediction. In: Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130. 2008. p. 644–55.
• Zhou B, Pei J, Tang Z. A spamicity approach to web spam detection. In: Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130. 2008. p. 277–88.
• Zhou B, Pei J, Luk W. A brief survey on anonymization techniques for privacy preserving publishing of social network data. ACM Sigkdd Explorations Newsletter. 2008;10:12–22.
• Wong RCW, Fu AWC, Pei J, Ho YS, Wong T, Liu Y. Efficient skyline querying with variable user preferences on nominal attributes. Proceedings of the VLDB Endowment. 2008 Jan 1;1(1):1032–43.
• Pei J, Hua M. Mining uncertain and probabilistic data: problems, challenges, methods, and applications. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 2008.
• Pei J, Huat M, Tao Y, Lin X. Query answering techniques on uncertain and probabilistic data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2008. p. 1357–63.
• Hua M, Pei J. A survey of utility-based privacy-preserving data transformation methods. In: Privacy-Preserving Data Mining. Springer, Boston, MA; 2008. p. 207–37.
• Pei J, Hua M, Tao Y, Lin X. Query answering techniques on uncertain and probabilistic data: tutorial summary. In: Proceedings of the 2008 ACM SIGMOD international conference on Management of data. 2008. p. 1357–64.
• Xu Y, Wang K, Fu AW-C, She R, Pei J. Privacy-preserving data stream classification. In: Privacy-Preserving Data Mining. Springer, Boston, MA; 2008. p. 487–510.
• Xu Y, Wang K, Fu AW-C, She R, Pei J. Privacy-preserving data stream classification. In: Privacy-Preserving Data Mining. Springer, Boston, MA; 2008. p. 487–510.
• Pei J, Hua M, Tao Y, Lin X. Query answering techniques on uncertain and probabilistic data: tutorial summary. In: Proceedings of the 2008 ACM SIGMOD international conference on Management of data. 2008. p. 1357–64.
• Wang H, Pei J. ?? ????????? 2008;23:481–96.
• Varde A, Pei J. Advances in information and knowledge management. In: ACM SIGIR Forum. 2008. p. 29–35.
• Wong RCW, Fu AWC, Pei J, Wang K. Mining favorable facets. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2007. p. 804–13.
• Hua M, Pei J. Cleaning disguised missing data: A heuristic approach. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2007. p. 950–8.
• Jian FMF, Pei J, Fu AWC. IX-cubes: Iceberg cubes for data warehousing and OLAP on XML data. In: International Conference on Information and Knowledge Management, Proceedings. 2007. p. 905–8.
• Acharya M, Xie T, Pei J, Xu J. Mining API patterns as partial orders from source code: From usage scenarios to specifications. In: 6th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, ESEC/FSE 2007. 2007. p. 25–34.
• Pei J, Xu J, Wang Z, Wang W, Wang K. Maintaining K-anonymity against incremental updates. In: Proceedings of the International Conference on Scientific and Statistical Database Management, SSDBM. 2007.
• Ning G, Juntao C, Wei Y, Haixun W, Jian P. A system framework for web service semantic and automatic orchestration. In: 2007 2nd International Conference on Pervasive Computing and Applications, ICPCA’07. 2007. p. 606–11.
• Pei J, Lau MKM, Yu PS. TS-trees: A non-alterable search tree index for trustworthy databases on Write-Once-Read-Many (WORM) storage. In: Proceedings - International Conference on Advanced Information Networking and Applications, AINA. 2007. p. 54–61.
• Xie T, Pei J, Hassan AE. Mining software engineering data. In: Proceedings - International Conference on Software Engineering. 2007. p. 172–3.
• Pei J, Fu AWC, Lin X, Wang H. Computing compressed multidimensional skyline cubes efficiently. In: Proceedings - International Conference on Data Engineering. 2007. p. 96–105.
• Tao Y, Xiao K, Pei J. Efficient skyline and top-k retrieval in subspaces. IEEE Transactions on Knowledge and Data Engineering. 2007 Aug 1;19(8):1072–88.
• Liu C, Wu K, Pei J. An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation. IEEE Transactions on Parallel and Distributed Systems. 2007 Jul 1;18(7):1010–23.
• Pei J, Han J, Lu H, Nishio S, Tang S, Yang D. H-Mine: Fast and space-preserving frequent pattern mining in a large databases. IIE Transactions (Institute of Industrial Engineers). 2007 Jun 1;39(6):593–605.
• Pei J, Han J, Wang W. Constraint-based sequential pattern mining: The pattern-growth methods. Journal of Intelligent Information Systems. 2007 Apr 1;28(2):133–60.
• Cho M, Pei J, Wang K. Answering ad hoc aggregate queries from data streams using prefix aggregate trees. Knowledge and Information Systems. 2007 Jan 1;12(3):301–29.
• Varde A, Pei J, others. Proceedings of the ACM first Ph. D. Workshop in CIKM. 2007;
• Xiong Z, Wang W, Pei J. Active rules termination analysis through conditional formula containing updatable variable. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2007. p. 281–92.
• Chen L, Zeng J, Pei J. Classifying noisy and incomplete medical data by a differential latent semantic indexing approach. In: Springer Optimization and Its Applications. 2007. p. 169–76.
• Varde A, Pei J. PIKM 2007 foreword. In: International Conference on Information and Knowledge Management, Proceedings. 2007.
• Hua M, Pei J, Fu AWC, Lin X, Leung HF. Efficiently answering top-k typicality queries on large databases. In: 33rd International Conference on Very Large Data Bases, VLDB 2007 - Conference Proceedings. 2007. p. 890–901.
• Liu Y, Chen L, Pei J, Chen Q, Zhao Y. Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays. In: Proceedings - Fifth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007. 2007. p. 37–46.
• Wong RC-W, Liu Y, Yin J, Huang Z, Fu AW-C, Pei J. (&ALPHA-anonymity based privacy preservation by lossy join. In: ADVANCES IN DATA AND WEB MANAGEMENT, PROCEEDINGS. 2007. p. 733-+.
• Wong RCW, Liu Y, Yin J, Huang Z, Fu AWC, Pei J. (α, k)-anonymity based privacy preservation by lossy join. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2007. p. 733–44.
• Bu Y, Leung TW, Fu AWC, Keogh E, Pei J, Meshkin S. WAT: Finding top-K discords in time series database. In: Proceedings of the 7th SIAM International Conference on Data Mining. 2007. p. 449–54.
• Wong RCW, Fu AWC, Wang K, Pei J. Minimality attack in privacy preserving data publishing. In: 33rd International Conference on Very Large Data Bases, VLDB 2007 - Conference Proceedings. 2007. p. 543–54.
• Pei J, Jiang B, Lin X, Yuan Y. Probabilistic skylines on uncertain data. In: 33rd International Conference on Very Large Data Bases, VLDB 2007 - Conference Proceedings. 2007. p. 15–26.
• Dong G, Pei J. Mining partial orders from sequences. In: Sequence Data Mining. Springer, Boston, MA; 2007. p. 89–112.
• Han J, Cai YD, Chen Y, Dong G, Pei J, Wah BW, et al. Multi-dimensional analysis of data streams using stream cubes. In: Data Streams. Springer, Boston, MA; 2007. p. 103–25.
• Chi-Wing Wong R, Fu W-C, Pei J, Ho YS, Wong T, Liu Y, et al. Efficient Skyline Querying with Variable User Preferences on Nominal Attributes. arXiv e-prints. 2007;arXiv-0710.
• Ng RT, Pei J. Introduction to the special issue on data mining for health informatics. ACM SIGKDD Explorations Newsletter. 2007;9:1–2.
• Dong G, Pei J. Distinguishing Sequence Patterns. In: Sequence Data Mining. Springer, Boston, MA; 2007. p. 113–30.
• Jiang D, Pei J, Ramanathan M, Lin C, Tang C, Zhang A. Mining gene-sample-time microarray data: A coherent gene cluster discovery approach. Knowledge and Information Systems. 2007 Jan 1;13(3):305–35.
• Wong RC-W, Fu AW-C, Pei J, Ho YS, Wong T, Liu Y. Efficient skyline querying with variable user preferences on nominal attributes. arXiv preprint arXiv:07102604. 2007;
• Dong G, Pei J. Sequence data mining. Springer Science & Business Media; 2007.
• Dong G, Pei J. Sequence Motifs: Identifying and Characterizing Sequence Families. In: Sequence Data Mining. Springer, Boston, MA; 2007. p. 67–87.
• Dong G, Pei J. Frequent and closed sequence patterns. In: Sequence data mining. Springer, Boston, MA; 2007. p. 15–46.
• Varde A, Pei J. PIKM 2007 foreword. In: International Conference on Information and Knowledge Management, Proceedings. 2007. p. iii–iii.
• Dong G, Pei J. Classification, clustering, features and distances of sequence data. In: Sequence data mining. Springer, Boston, MA; 2007. p. 47–65.
• Zhou B, Pei J. Sketching landscapes of page farms. In: Proceedings of the 7th SIAM International Conference on Data Mining. 2007. p. 593–8.
• Chen Y, Dong G, Han J, Pei J, Wah BW, Wang J. Regression cubes with lossless compression and aggregation. IEEE Transactions on Knowledge and Data Engineering. 2006 Dec 1;18(12):1585–98.
• Xie T, Pei J. MAPO: Mining API usages from open source repositories. In: Proceedings - International Conference on Software Engineering. 2006. p. 54–7.
• On BW, Elmacioglu E, Lee D, Kang J, Pei J. An effective approach to entity resolution problem using Quasi-Clique and its application to digital libraries. In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries. 2006. p. 51–2.
• Xu Y, Wang K, Fu AWC, She R, Pei J. Classification spanning correlated data streams. In: International Conference on Information and Knowledge Management, Proceedings. 2006. p. 132–41.
• Pei J, Yuan Y, Lin X, Jin W, Ester M, Liu Q, et al. Towards multidimensional subspace skyline analysis. In: ACM Transactions on Database Systems. 2006. p. 1335–81.
• Li J, Li H, Wong L, Pei J, Dong G. Minimum description length principle: Generators are preferable to closed patterns. In: Proceedings of the National Conference on Artificial Intelligence. 2006. p. 409–14.
• Pekerskaya I, Pei J, Wang K. Mining changing regions from access-constrained snapshots: A cluster-embedded decision tree approach. In: Journal of Intelligent Information Systems. 2006. p. 215–42.
• Huang Y, Pei J, Xiong H. Mining co-location patterns with rare events from spatial data sets. GeoInformatica. 2006 Sep 1;10(3):239–60.
• Wang J, Han J, Pei J. Closed constrained gradient mining in retail databases. IEEE Transactions on Knowledge and Data Engineering. 2006 Jun 1;18(6):764–9.
• Wang H, Yin J, Pei J, Yu PS, Yu JX. Suppressing model overfitting in mining concept-drifting data streams. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2006. p. 736–41.
• On BW, Elmacioglu E, Lee D, Kangt J, Pei J. Improving grouped-entity resolution using Quasi-Cliques. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2006. p. 1008–15.
• Zhu W, Pei J, Yin J, Xie Y. Granularity adaptive density estimation and on demand clustering of concept-drifting data streams. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2006. p. 322–31.
• Aggarwal CC, Pei J, Zhang B. On privacy preservation against adversarial data mining. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2006. p. 510–6.
• Pei J, Yu PS, Wang K. Discovering frequent closed partial orders from strings. IEEE Transactions on Knowledge and Data Engineering. 2006 Jan 1;18(11):1467–81.
• Xu J, Wang W, Pei J, Wang X, Shi B, Fu AWC. Utility-based anonymization using local recoding. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2006. p. 785–90.
• Han J, Kamber M, Pei J. Data preprocessing. Data mining: concepts and techniques. San Francisco: Morgan Kaufmann; 2006.
• Pei J, Han J, Lakshmanan LVS. Erratum: Pushing convertible constraints in frequent itemset mining (Data Mining and Knowledge Discovery: An International Journal (May 2004) 8:3 (227-252)). Data Mining and Knowledge Discovery. 2006 Jan 1;12(1):119.
• Zhang J, Zhou X, Wang W, Shi B, Pei J. Using High Dimensional Indexes to Support Relevance Feedback Based Interactive Images Retrieval∗. In: VLDB 2006 - Proceedings of the 32nd International Conference on Very Large Data Bases. 2006. p. 1211–4.
• Jiuyong L, Wong RCW, Fu AWC, Jian P. Achieving k-anonymity by clustering in attribute hierarchical structures. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2006. p. 405–16.
• Pei J, Han J, Lakshmanan LVS. An Erratum on" Pushing Convertible Constraints in Frequent Itemset Mining". Data Min Knowl Discov. 2006;12:119–119.
• Pei J, Han J, Lakshmanan LVS. An Erratum on" Pushing Convertible Constraints in Frequent Itemset Mining". Data Min Knowl Discov. 2006;12:119–119.
• Tao Y, Xiao K, Pei J. SUBSKY: Efficient computation of skylines in subspaces. In: Proceedings - International Conference on Data Engineering. 2006. p. 66–75.
• Xu J, Wang W, Pei J, Wang X, Shi B, Fu AW-C. Utility-based anonymization for privacy preservation with less information loss. Acm Sigkdd Explorations Newsletter. 2006;8:21–30.
• Wong RC-W, Fu AW-C, Pei J, Wang K, Wan SC-W, Lo CS. Multidimensional k-anonymization by linear clustering using space-filling curves. Simon Fraser University School of Computing Science Technical Report. 2006;
• Han J, Kamber M, Pei J. Data mining: concepts and techniques Morgan Kaufmann. San Francisco. 2006;
• Yan J, Jian P. Web-based massive data processing workshop chairs' message. Vol. 4256 LNCS. 2006.
• Tao Y, Xiao X, Pei J. Subsky: Efficient computation of skylines in subspaces. In: 22nd International Conference on Data Engineering (ICDE’06). 2006. p. 65–65.
• Pei J, Jiang D, Zhang A. Mining cross-graph quasi-cliques in gene expression and protein interaction data. In: Proceedings - International Conference on Data Engineering. 2005. p. 353–4.
• Wang H, Pei J, Yu PS. Online mining of data streams: Applications, techniques and progress. In: Proceedings - International Conference on Data Engineering. 2005. p. 1146.
• Liu C, Wu K, Pei J. A dynamic clustering and scheduling approach to energy saving in data collection from wireless sensor networks. In: 2005 Second Annual IEEE Communications Society Conference on Sensor and AdHoc Communications and Networks, SECON 2005. 2005. p. 374–85.
• Yu H, Pei J, Tang S, Yang D. Mining the most general multidimensional summarization of "probable groups" in data warehouses. In: Proceedings of the International Conference on Scientific and Statistical Database Management, SSDBM. 2005. p. 185–94.
• Wang W, Wang C, Zhu Y, Shi B, Pei J, Yan X, et al. GraphMiner: A structural pattern-mining system for large disk-based graph databases and its applications. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2005. p. 879–81.
• Pei J, Jiang D, Zhang A. On mining cross-graph quasi-cliques. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2005. p. 228–38.
• Pei J, Liu J, Wang H, Wang K, Yu PS, Wang J. Efficiently mining frequent closed partial orders (extended abstract). In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2005. p. 753–6.
• Ye J, Zhou X, Pei J, Chen L, Zhang L. A stratification-based approach to accurate and fast image annotation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005. p. 284–96.
• Pei J, Jin W, Ester M, Tao Y. Catching the best views of skyline: A semantic approach based on decisive subspaces. In: VLDB 2005 - Proceedings of 31st International Conference on Very Large Data Bases. 2005. p. 253–64.
• Jiang D, Pei J, Zhang A. An interactive approach to mining gene expression data. IEEE Transactions on Knowledge and Data Engineering. 2005 Oct 1;17(10):1363–78.
• Han J, Chen Y, Dong G, Pei J, Wah BW, Wang J, et al. Scream cube: An architecture for multi-dimensional analysis of data streams. Distributed and Parallel Databases. 2005 Sep 1;18(2):173–97.
• Wang H, Pei J, Yu PS. Pattern-based similarity search for microarray data. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2005. p. 814–9.
• Aggarwal CC, Pei J. A Framework for Adversarial Privacy Preserving Data Mining. TR 2005–08. 2005;
• Han J, Pei J, Yan X. Sequential pattern mining by pattern-growth: Principles and extensions. In: Foundations and Advances in Data Mining. Springer, Berlin, Heidelberg; 2005. p. 183–220.
• Cho M, Pei J, Wang H, Wang W. Preference-Based Frequent Pattern Mining. International Journal of Data Warehousing and Mining (IJDWM). 2005 Jan 1;1(4):56–77.
• Wang H, Pei J. A random method for quantifying changing distributions in data streams. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005. p. 684–91.
• Jiang D, Pei J, Zhang A. A general approach to mining quality pattern-based clusters from microarray data. In: Lecture Notes in Computer Science. 2005. p. 188–200.
• Cho M, Pei J, Cheung DW. Cross table cubing: Mining iceberg cubes from data warehouses. In: Proceedings of the 2005 SIAM International Conference on Data Mining, SDM 2005. 2005. p. 461–5.
• Dong G, Jiang C, Pei J, Li J, Wong L. Mining succinct systems of minimal generators of formal concepts. In: Lecture Notes in Computer Science. 2005. p. 175–87.
• Wang H, Pei J, Yu PS. Online mining data streams: Problems, applications and progress. In: Proc the 21st International Conference on Data Engineering, ICDE�05. 2005. p. 5–8.
• Pei J, Jin W, Ester M, Tao Y. Catching the best views of skyline: A semantic approach based on decisive subspaces. Space (X, Y). 2005;100:1–1.
• Pei J, Liu J, Wang H, Wang K, Yu PS, Wang J. Efficiently mining frequent closed partial orders. In: Fifth IEEE International Conference on Data Mining (ICDM’05). 2005. p. 4-pp.
• Han J, Chen Y, Dong G, Pei J, Wah BW, Wang J, et al. Stream cube: An architecture for multi-dimensional analysis of data streams. Distributed and Parallel Databases. 2005;18:173–97.
• Zhang D, Tong Y, Tang S, Yang D. Advanced Data Mining and Applications. Springer Berlin, Germany:; 2005.
• Ramakrishnan R, Agrawal R, Freytag J-C, Bollinger T, Clifton CW, Dzeroski S, et al. Data mining: The next generation. In: Dagstuhl Seminar Proceedings. 2005.
• Deng L, Ma J, Pei J. Rank sum method for related gene selection and its application to tumor diagnosis. Chinese Science Bulletin. 2004 Dec 1;49(15):1652–7.
• Pei J, Han J, Mortazavi-Asl B, Wang J, Pinto H, Chen Q, et al. Mining sequential patterns by pattern-growth: The prefixspan approach. IEEE Transactions on Knowledge and Data Engineering. 2004 Nov 1;16(11):1424–40.
• Wang H, Chu F, Fan W, Philip SY, Pei J. A fast algorithm for subspace clustering by pattern similarity. In: Proceedings of the International Conference on Scientific and Statistical Database Management, SSDBM. 2004. p. 51–60.
• Dong G, Han J, Lam JMW, Pei J, Wang K, Zou W. Mining constrained gradients in large databases. IEEE Transactions on Knowledge and Data Engineering. 2004 Aug 1;16(8):922–38.
• Pei J, Han J, Lakshmanan LVS. Pushing convertible constraints in frequent itemset mining. Data Mining and Knowledge Discovery. 2004 May 1;8(3):227–52.
• Pei J, Dong G, Zou W, Han J. Mining condensed frequent-pattern bases. Knowledge and Information Systems. 2004;6:570–94.
• Jiang D, Pei J, Ramanathan M, Tang C, Zhang A. Mining coherent gene clusters from gene-sample-time microarray data. In: KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2004. p. 430–9.
• Deng L, Pei J, Ma J, Lee DL. A rank sum test method for informative gene discovery. In: KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2004. p. 410–9.
• Wang C, Wang W, Pei J, Zhu Y, Shi B. Scalable mining of large disk-based graph databases. In: KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2004. p. 316–25.
• Han J, Pei J, Yin Y, Mao R. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery. 2004 Jan 1;8(1):53–87.
• Han JW, Pei J, Yan XF. From sequential pattern mining to structured pattern mining: A pattern-growth approach. Journal of Computer Science and Technology. 2004 Jan 1;19(3):257–79.
• Yang D, Tang S, Pei J, Wang T, Gao J. First International Workshop on Conceptual Model-Directed Web Information Integration and Mining (CoMWIM 2004)-Preface to CoMWIM 2004. Lecture Notes in Computer Science. 2004;3289:197–197.
• Pei J, Upadhyaya SJ, Farooq F, Govindaraju V. Data mining for intrusion detection: techniques, applications and systems. In: Proceedings 20th International Conference on Data Engineering. 2004. p. 877–877.
• Jiang D, Pei J, Zhang A. GPX: interactive mining of gene expression data. In: Proceedings of the Thirtieth international conference on Very large data bases-Volume 30. 2004. p. 1249–52.
• Han J, Kamber M. Adatbányászat. Koncepciók és technikák Panem Könyvkiadó, Budapest. 2004;
• Jiang D, Zhang A, Pei J. GPX. In: Proceedings 2004 VLDB Conference: The 30th International Conference on Very Large Databases (VLDB). 2004. p. 1249–52.
• Wang C, Hong M, Pei J, Zhou H, Wang W, Shi B. Efficient pattern-growth methods for frequent tree pattern mining. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2004. p. 441–51.
• Jiang D, Pei J, Ramanathan M, Tang C, Zhang A. Mining coherent gene clusters from three-dimensional microarray data. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD�04). 2004. p. 22–5.
• Han J, Pei J, Yin Y, Mao R. Methods and system for mining frequent patterns. 2003.
• Tang C, Zhang A, Pei J. Mining phenotypes and informative genes from gene expression data. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2003. p. 655–60.
• Wang J, Han J, Pei J. CLOSET+: Searching for the best strategies for mining frequent closed itemsets. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2003. p. 236–45.
• Pei J, Zhang X, Cho M, Wang H, Yu PS. MaPle: A fast algorithm for maximal pattern-based clustering. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2003. p. 259–66.
• Lakshmanan LVS, Pei J, Zhao Y. SOCQET: Semantic OLAP with Compressed Cube and Summarization. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2003. p. 658.
• Lakshmanan LVS, Pei J, Zhao Y. QC-Trees: An Efficient Summary Structure for Semantic OLAP. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2003. p. 64–75.
• Jiang D, Pei J, Zhang A. Interactive exploration of coherent patterns in time-series gene expression data. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2003. p. 565–70.
• Jiang D, Pei J, Zhang A. Towards interactive exploration of gene expression patterns. ACM SIGKDD Explorations Newsletter. 2003;5:79–90.
• Huang Y, Xiong H, Shekhar S, Pei J. Mining confident co-location rules without a support threshold. In: Proceedings of the ACM Symposium on Applied Computing. 2003. p. 497–501.
• Jiajia M, Heng M, Yuemin W, Hongmei L, Jianming P. Vasodilation effect of puerarin on abdominal aortic artery in the rat and the underlying mechanism. Journal of the Fourth Military Medical University. 2003;24:2231–4.
• Chen Z, Chen L, Pei J, Tao Y, Wang H, Wang W, et al. Recent Progress on Selected Topics in Database Research - A Report by Nine Young Chinese Researchers Working in the United States. J Comput Sci Technol. 2003;18:538–52.
• Chen Z, Li C, Pei J, Tao Y, Wang H, Wang W, et al. Recent Progress on Selected Topics in Database Research - A Report by Nine Young Chinese Researchers Working in the United States. Journal of Computer Science and Technology. 2003 Jan 1;18(5):538–52.
• Jiang D, Pei J, Zhang A. DHC: A density-based hierarchical clustering method for time series gene expression data. In: Proceedings - 3rd IEEE Symposium on BioInformatics and BioEngineering, BIBE 2003. 2003. p. 393–400.
• Lakshmanan LVS, Pei J, Zhao Y. Efficacious data cube exploration by semantic summarization and compression. In: Proceedings - 29th International Conference on Very Large Data Bases, VLDB 2003. 2003. p. 1125–8.
• Lakshmanan LVS, Pei J, Zhao Y. Efficacious Data Cube Exploration by Semantic Summarization and Compression. In: Proceedings 2003 VLDB Conference: 29th International Conference on Very Large Databases (VLDB). 2003. p. 1125–8.
• Dong G, Han J, Lakshmanan LVS, Pei J, Wang H, Yu PS. Online mining of changes from data streams: Research problems and preliminary results. In: Proceedings of the 2003 ACM SIGMOD Workshop on Management and Processing of Data Streams. 2003. p. 739–47.
• Giannella C, Han J, Pei J, Yan X, Yu PS. Mining frequent patterns in data streams at multiple time granularities. Next generation data mining. 2003;212:191–212.
• Kum H-C, Pei J, Wang W, Duncan D. ApproxMAP: Approximate mining of consensus sequential patterns. In: Proceedings of the 2003 SIAM International Conference on Data Mining. 2003. p. 311–5.
• Pei J. A general model for online analytical processing of complex data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2003 Jan 1;2813:321–34.
• Pei J, Dong G, Zou W, Han J. On computing condensed frequent pattern bases. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2002. p. 378–85.
• Wang T, Tang S, Yang D, Gao J, Wu Y, Pei J. COMMIX: Towards effective web information extraction, integration and query answering. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2002. p. 620.
• Han J, Wang J, Dong G, Pei J, Wang K. CubeExplorer: Online exploration of data cubes. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2002. p. 626.
• Wang T, Tang S, Yang D, Gao J, Wu Y, Pei J. COMMIX: Towards Effective Web Information Extraction, Integration and Query Answering. In: Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, SIGMOD 2002. 2002. p. 620.
• Han J, Wang J, Dong G, Pei J, Wang K. CubeExplorer: Online Exploration of Data Cubes. In: Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, SIGMOD 2002. 2002. p. 626.
• Pei J, Han J-W, Wang W. Constraint-based sequential pattern mining in large databases. In: Proc 2002 Int�l Conf Information and Knowledge Management (CIKM�02). 2002. p. 18–25.
• Pei J, Han J, Wang W. Mining sequential patterns with constraints in large databases. In: International Conference on Information and Knowledge Management, Proceedings. 2002. p. 18–25.
• Pei J. PATTERN-GROWTH METHODS FOR FREQUENT. 2002.
• Chen Y, Dong G, Han J, Pei J, Wah BW, Wang J. Online Analytical Processing Stream: Is it Feasible? 2002;
• Chen Y, Dong G, Han J, Pei J, Wah BW, Wang J. Olaping stream data: Is it feasible. In: Proc Workshop on Research Issues in Data Mining and Knowledge Discovery, ACM SIGMOD. 2002. p. 53–8.
• Pei J, Han J. Constrained frequent pattern mining: a pattern-growth view. ACM SIGKDD Explorations Newsletter. 2002;4:31–9.
• Chen Y, Dong G, Han J, Pei J, Wah BW, Wang J. Online analytical processing stream data: Is it feasible? In: DMKD. 2002.
• Lakshmanan LVS, Pei J, Han J. Quotient cube: How to summarize the semantics of a data cube. In: VLDB’02: Proceedings of the 28th International Conference on Very Large Databases. 2002. p. 778–89.
• Li W, Han J, Pei J. CMAR: Accurate and efficient classification based on multiple class-association rules. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2001. p. 369–76.
• Pei J, Han J, Lu H, Nishio S, Tang S, Yang D. H-mine: Hyper-structure mining of frequent patterns in large databases. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2001. p. 441–8.
• Han J, Jamil H, Lu Y, Chen L, Liao Y, Pei J. DNA-Miner: A system prototype for mining DNA sequences. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2001. p. 618.
• Pei J, Han J, Lakshmanan LVS. Mining frequent itemsets with convertible constraints. Proceedings - International Conference on Data Engineering. 2001 Jan 1;433–42.
• Pei J, Han J, Mortazavi-Asl B, Pinto H, Chen Q, Dayal U, et al. PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings - International Conference on Data Engineering. 2001. p. 215–24.
• Han J, Pei J, Dong G, Wang K. Efficient computation of iceberg cubes with complex measures. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2001. p. 1–12.
• Han J, Pei J, Dong G, Wang K. Efficient computation of iceberg cubes with complex measures. SIGMOD Record (ACM Special Interest Group on Management of Data). 2001 Jan 1;30(2):1–12.
• Han J, Kamber M, Pei J. Data mining: concepts and technologies. Data Mining Concepts Models Methods & Algorithms. 2001;5:1–18.
• Dong G, Han J, Lam J, Pei J, Wang K. Mining multi-dimensional constrained gradients in data cubes. In: VLDB 2001 - Proceedings of 27th International Conference on Very Large Data Bases. 2001. p. 321–30.
• Pinto H, Han J, Pei J, Wang K, Chen Q, Dayal U. Multi-dimensional sequential pattern mining. In: International Conference on Information and Knowledge Management, Proceedings. 2001. p. 81–8.
• Hau J, Lu Y, Liao Y, Pei J, Jamil H, Chen L. DNA-Miner: A System Prototype for Mining DNA Sequences. SIGMOD Record. 2001 Jan 1;30(2):618.
• Pei J, Tung AKH, Han J. Fault-Tolerant Frequent Pattern Mining: Problems and Challenges. DMKD. 2001;1:270–270.
• Han J, Pei J. Pattern growth methods for sequential pattern mining: Principles and extensions. In: Workshop on Temporal Data Mining, 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD�01) ACM Press. 2001.
• Han J, Lakshmanan LVS, Pei J. Scalable frequent-pattern mining methods: an overview. In: Tutorial notes of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. 2001. p. 5–1.
• Han J, Pei J, Mortazavi-Asl B, Chen Q, Dayal U, Hsu MC. FreeSpan: Frequent pattern-projected sequential pattern mining. In: Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2000. p. 355–9.
• Pei J, Han J, Mao R, others. CLOSET: An efficient algorithm for mining frequent closed itemsets. In: ACM SIGMOD workshop on research issues in data mining and knowledge discovery. 2000. p. 21–30.
• Han J, Pei J, Yin Y. Mining Frequent Patterns without Candidate Generation. In: SIGMOD 2000 - Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. 2000. p. 1–12.
• Pei J, Han J, Mortazavi-Asl B. Zhu. H. 2000. Mining access patterns efficiently from Web Logs. In: Proceedings of. 2000.
• Pei J, Han J. Can we push more constraints into frequent pattern mining? In: Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2000. p. 350–4.
• Pei J, Mao R, Hu K, Zhu H. Tow ards Data Mining Benchmarking: A Test Bed for Performance Study of Frequent Pattern Mining. SIGMOD Record. 2000 Jan 1;29(2):592.
• Han J, Pei J, Yin Y. Mining frequent patterns without candidate generation. SIGMOD Record (ACM Special Interest Group on Management of Data). 2000 Jan 1;29(2):1–12.
• Pei J, Han J, Mortazavi-Asl B, Zhu H. Mining access patterns efficiently from web logs. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2000. p. 396–407.
• Han J, Pei J. Mining frequent patterns by pattern-growth: methodology and implications. ACM SIGKDD explorations newsletter. 2000;2:14–20.
• Han J. Frequent pattern-projected sequential pattern mining. Proc of the ACM SIGKDD, 2000. 2000;
• Pei J, Chai W, Zhao C, Tang S, Yang D. Algebra for online analytical processing data cube. Acta Metallurgica Sinica (English Letters). 1999 Oct 1;12(5):561–9.
• Han J, Pei J, Yin Y. Mining partial periodicity using frequent pattern trees. CS Tech. Rep. 99-10, Simon Fraser University; 1999.
• Tang C, Zhang A, Pei J. Genes. 1997;
• Pei Z, Chen J, Zhu M, Pei J, Liu Z, Tan Y. Plasma levels of endothelin-1, nitric oxide, malondialdehyde and superoxide dismutase in rats exposed to infrasound. Chinese Journal of Pathophysiology. 1986;
• Huang F, Gao S, Pei J, Huang H. Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization Download PDF.
• Jamil H, Pei J, Cuzzocrea A. Session details: Artificial intelligence & agents, information systems, and software development: Data mining track.
• Pei J, Han J, Mortazavi-asl B, Pinto H, Chen Q, Dayal U. chun Hsu, M.(2001). Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: ICDE�01: Proceedings of the 2001 International Conference on Data Engineering. p. 215–215.
• Huang F, Gao S, Pei J, Huang H. Momentum-Based Policy Gradient Methods Download PDF.
• Discovery P-ACOK, Mining D, Pei J. Advances in knowledge discovery and data mining: 17th Pacific-Asia conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013: proceedings. Springer;
• Bonchi F, Cao L, Chen M-S, Cook DJ, Gaussier E, Goethals B, et al. DSAA 2019.
• Han J, Kamber M, Pei J. 4 Major Tasks in Data Preprocessing.
• Bailey BANSJ, Pei J. Document Clustering of Scientific Texts Using Citation Contexts.
• Xu Y, Wang K, Fu AW-C, She R, Pei J. Privacy-Preserving Classification for Data Streams.
• Pei J. Towards Trustworthy Data Science: Interpretability, Fairness and Marketplaces.
• Bonchi F, Cao L, Chen M-S, Cook DJ, Gaussier E, Goethals B, et al. Steering Committee DSAA 2020.
• Tao Y, Li J, Ding L, Lin X, Pei J. On Representing Skylines by Distance.
• Pei J, Han J, Mortazavi-asl B, Zhu H. Mining Access Patterns E ciently from Web Logs? In: Proc of PAKDD’OO.
• Crago S, Dustdar S, Foster I, Fox G, Guo M, Herrera F, et al. BigDataSE 2021.
• Wang H, Xu C, Zhang C, Xu J, Peng Z, Pei J. vChain+: Optimizing Verifiable Blockchain Boolean Range Queries.
• Chu HWF, Fan W, Philip SY, Pei J. A Fast Algorithm for Subspace Clustering by Pattern Similarity. In: Proceedings of the 16th International Conference on Scientific and Statistical Database Management (SSDBM�04). p. 20–00.
• Pei J. SPOTLIGHT ON TRANSACTIONS.
• Pei J, Ariwala SR, Jiang D. Online mining changes of clusters in data streams. Submitted for publication.
• Cui J, Pei J, Gama JA. The International Conference on Advanced Data Mining And Application ADMA2009.
• Bailey J, Berendt B, Cao L, Chawla AN, Chen X, Diesner J, et al. Ulrik Brandes, University of Konstanz, Germany Piotr Br6dka, Wroc1aw University of Technology, Poland Yi Cai, South China University of Technology, China.
• Pei J, Wang K. Online Skyline Analysis with Dynamic Preferences on Nominal Attributes.
• Kumar R, Toivonen H, Pei J, Huang JZ, Wu X. ICDM 2014 14th IEEE International Conference on Data Mining Shenzhen, China/14-17 December 2014.
• Cercone N, Cheng X, Hu X, Nambiar R, Pei J, Raghavan V, et al. Big Data Steering Committee.
• Wong RC-W, Fu AW-C, Pei J, Wang K, Wan SC-W, Lo CS. Multidimensional k-Anonymization by Linear Clustering Using Space-Filling Curves Technical Report TR 2006-27.
• Yang Y, Mao X, Pei J, He X. Continuous Influence Maximization: Algorithmic Framework and Implementation under Triggering Models.
• Chen J, Yang LT, Kotagiri R, Pei J, Sun X-H, Sahni S, et al. DSS 2017.
• Pei J, Upadhyaya SJ, Farooq F, Govindaraju V. Data Mining for Intrusion Detection.
• Kung S-Y, Zomaya A, Qiu M, Yan Z, Wu Y, Chen J, et al. Committee Members BigDataSE 2018.
• Cook D, Pei J, Wang W, Zaïane O, Wu X. 11th IEEE International Conference on Data Mining.
• Wong RC-W, Fu AW-C, Wang K, Pei J. Minimality Attack in Privacy Preserving Data Publishing Technical Report TR 2006-28.
• Chomicki J, Pei J, Srihari RK, Yang G, Zhang A. Database Research at University at Buffalo.
• Jiang B, Pei J, Lin X, Cheung DW, Han J. Mining Preferences from Superior and Inferior Examples TR 2008-09.
• Pei J, Zhou B, Tang Z, Huang D. Data Mining Techniques for Web Spam Detection. Simon Fras University Microsoft Ad Center.
• Zhou Z-H, Wang W, Kumar R, Toivonen H, Pei J, Huang JZ, et al. ICDMW 2014 14th IEEE International Conference on Data Mining Workshops Shenzhen, China/14 December 2014.
• Steinbach M, Kumar V, Han J, Kamber M, Pei J. MSCIT 5210: Knowledge Discovery and Data Mining.
• Pei J, Han J, Mao R. CLOSET: An E cient Algorithm for Mining Frequent Closed Itemsets. In: ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. p. 21–30.
• Pei J, Xiong H. Mining Co-location Patterns with Rare Events from Spatial Data Sets* Yan Huang (huangyan@ unt. edu) Dept. of Computer Science and Engineering, University of North Texas, USA.
• Pei J, Hua M, Tao Y, Lin X. Mining Uncertain and Probabilistic Data.
• Pei J. The Pattern-based Approaches to Mining Microarray Data.
• Liao Z, Jiang D, Pei J, Kang D, Sun X, Choi H-J, et al. Constructing a Multidimensional Topic-Concept Cube for OLAP on Large-Scale Search Logs.
• Fox GC, Sun X-H, Pei J, Zhang X, Li X, Chen J, et al. BigDataMR2012 Organizing and Program Committees.
• Chen Y, Dong G, Pei J, Wah BW, Wang J. Stream C ube: A n A rchitecture for.
• Chakrabarti D, Chang E, Chapelle O, Clifton C, Davison B, Ding C, et al. SIGKDD-2010 Program Committees.
• Xing Z, Pei J. Running Title: Exploring Disease Association from the NHANES Data.

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