NSF IIS 03-08215 Mining Dynamics of Data Stream in Multi-Dimensional

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National Science Foundation Award Number: IIS-0308215 (Sept. 2003-August 2006)

Contact Information

Jiawei Han, PI

Department of Computer Science
University of Illinois, Urbana-Champaign
1304 West Springfield Ave. , Urbana, Illinois 61801 U.S.A.
Office: (217) 333-6903, Fax: (217) 265-6494

E-mail: hanj at cs.uiuc.edu, URL: http://www.cs.uiuc.edu/~hanj

List of Supported Students and Staff

� Xifeng Yan, Ph.D. student, Department of Computer Science, University of Illinois at Urbana-Champaign

� Ying Lu, Ph.D. student, Department of Computer Science, University of Illinoisat Urbana-Champaign

� Jianyong Wang, research fellow, Department of Computer Science, University of Illinois at Urbana-Champaign

� Jiong Yang, research fellow, Department of Computer Science, University of Illinois at Urbana-Champaign

Project Award Information

Project Summary

Stream data processing and mining represent an important, emerging class of data-intensive applications where data flows in and out dynamically, in huge (possibly infinite) volumes, adaptive to only single-scan algorithms, but often demanding fast or even real-time responses. Based on the observation that a majority of stream data resides at the primitive abstraction level, but most interesting patterns may need to be discovered at certain high levels of abstraction in multi-dimensional space, we study the issues on stream data mining and develop effective, efficient and scalable methods for mining the dynamics of data streams in multi-dimensional space. The scope of our study includes the discovery of changes, trends, and evolutions of characteristics, clusters, classification models, and frequent patterns in data streams. Our methodology is to capture sufficient statistical, compact and aggregate information in concise data structures to facilitate the efficient processing of both continuous and ad-doc stream mining queries. Several strategically important applications will be explored, including network intrusion detection, telecommunication and Web data flow analysis, and financial data flow analysis. The study will contribute to the development of the principles and new methods for real-time data mining systems and promote its strategically important applications, including timely discovery of terrorist or criminal activities for homeland security protection, intrusion detection, multi-dimensional analysis of data-intensive, fast-changing events, and so on. The research results will be published timely for wide dissemination, industry adoption, and education of new generation of information technology students and workers.

Publications and Products:

Journal articles (including accepted)

  1. Dong Xin, Jiawei Han, Xiaolei Li, Zheng Shao, and Benjamin W. Wah, “Computing Iceberg Cubes by Top-Down and Bottom-Up Integration: The StarCubingApproach”, IEEE Transactions on Knowledge and Data Engineering, (accepted), Aug. 2006

  2. Chao Liu, Long Fei,Xifeng Yan, Jiawei Han, and Samuel P. Midkiff, “_SOBER: Statistical Model-based Fault Localization_”, IEEE Transactions on Software Engineering, (accept), Aug. 2006.

  3. Xifeng Yan, Feida Zhu, Philip S. Yu, and JiaweiHan, “_Feature-based Substructure Similarity Search_”, ACM Transactions on Database Systems, accept for publication, April 2006.

4. F. Pan, K. Kamath, K. Zhang, S. Pulapura, A. Achar, J. Nunez-Iglesias, Y. Huang, X. Yan, J. Han, H. Hu, M. Xu, X. J. Zhou. “_Integrative Array Analyzer: A software package for analysis of cross-platform and cross-species microarray data_”, Bioinformatics, 2006.

  1. J. Wang, J. Han, and J. Pei, “_Closed Constrained-Gradient Mining in Retail Databases_”, IEEE Transactions on Knowledge and Data Engineering, 18(6): 764-769, 2006.

  2. X. Yin, J. Han, J. Yang and P. S. Yu, “_Efficient Classification across Multiple Database Relations: A CrossMineApproach_”, IEEE Transactions on Knowledge and Data Engineering}, 18(6): 770-783, 2006.

  3. Charu Aggarwal,Jiawei Han, Jianyong Wang, and Philip S. Yu, “_A Framework for On-Demand Classification of Evolving Data Streams_”, IEEE Transactions on Knowledge and Data Engineering}, 18(5):577-789, 2006.

  4. Deng Cai, Xiaofei He, Jiawei Han and Hong-Jiang Zhang, “_Orthogonal Laplacianfaces for Face Recognition_”, IEEE Transactions on Image Processing, (accepted), March 2006.

  5. Hwanjo Yu, JiongYang, Jiawei Han, and XiaoleiLi, “_Making SVM Scalable to Large Data Sets Using Hierarchical Indexing_”, Data Mining and Knowledge Discovery, 11(3): 295-321, 2005.

  6. D. Xin, J. Han, X. Yan and H. Cheng, “_On Compressing Frequent Patterns_”, Knowledge and Data Engineering, (Special issue on Intelligent Data Mining), accepted in Nov. 2005.

  7. Jiawei Han, YixinChen, Guozhu Dong, JianPei, Benjamin W. Wah, JianyongWang, and Y. Dora Cai, “_Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams_”, Distributed and Parallel Databases, 18(2): 173-197, 2005.

  8. Xifeng Yan, Philip Yu, and Jiawei Han, “_Graph Indexing Based on Discriminative Frequent Structure Analysis_”, ACM Transactions on Database Systems, 30(4): 960-993 (2005).

  9. Deng Cai, Xiaofei He and Jiawei Han, “_Document Clustering Using Locality Preserving Indexing_”, IEEE Transactions on Knowledge and Data Engineering, 17(12):1624-1637, 2005.

  10. C. Aggarwal, J. Han, J. Wang, and P. S. Yu, “_On Efficient Algorithms for High Dimensional Projected Clustering of Data Streams_”,Data Mining and Knowledge Discovery, 10:251-272, 2005.

  11. J. Wang, J. Han, Y. Lu, and P. Tzvetkov, “TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets”, IEEE Transactions on Knowledge and Data Engineering}, 17(5):652-664, 2005.

  12. K. Wang, Y. Jiang, J. X. Yu, G. Dong, and J. Han, “_Divide-and-Approximate: A Novel Constraint Push Strategy for Iceberg Cube Mining_”, IEEE Transactions on Knowledge and Data Engineering, 17(3):354-368, 2005.

  13. J. Han, J. Pei, and X. Yan, “From Sequential Pattern Mining to Structured Pattern Mining: A Pattern-Growth Approach,” Journal of Computer Science and Technology, 19(3):257-279, 2004.

  14. J. Han, J. Pei, Y. Yin and R. Mao, “Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach,” Data Mining and Knowledge Discovery, 8(1):53-87, 2004.

19. H. Yu, J. Han, K. C.-C. Chang, “PEBL: Web Page Classification Without Negative Examples,” IEEE Transactions on Knowledge and Data Engineering (Special Issue on Mining and Searching the Web, 16(1):70-81, 2004.

  1. G. Dong, J. Han, J. Lam, J. Pei, K. Wang, and W. Zou, “Mining Constrained Gradients in Multi-Dimensional Databases,”IEEE Transactions on Knowledge and Data Engineering, 16(5):922-938, 2004.

  2. J. Pei, G. Dong, W. Zou, and J. Han, “Mining Condensed Frequent Pattern Bases,” Knowledge and Information Systems, 2004.

  3. J. Pei, J. Han, and L. V. S. Lakshmanan, “Pushing Convertible Constraints in Frequent Itemset Mining,”� Data Mining and Knowledge Discovery, 8(3):227-252, 2004.

  4. J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu, “Mining Sequential Patterns by Pattern-Growth: The PrefixSpanApproach,” IEEE Transactions on Knowledge and Data Engineering, 16(11):1424-1440, 2004.

  5. A. Doan, Y. Lu, Y. Lee, and J. Han, “Profile-Based Object Matching for Information Integration,” IEEE Intelligent System, 18(5):54-59, 2003.

  6. Y. Lu and J. Han, ``Cancer classification using gene expression data'',Information Systems (Special Issue on Data Management in Bioinformatics), 28(4): 243-268, 2003.

Book and Book Chapters

  1. Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques}, (Foreword by Jim Gray), 2nd ed., Morgan Kaufmann, 2006.

  2. Jiawei Han, Hector Gonzalez, Xiaolei Li, and Diego Klabjan, “Warehousing and Mining Massive RFID Data Sets”,� in Xue Li, Osmar R. Zaiane, Zhanhuai Li (eds.), Proc. 2006 Int. Conf. Advanced Data Mining and Applications (ADMA'06), Xi'An, China, August 2006, pp. 1-18. (Lecture Notes in Computer Science, Vol. 4093, Springer Berlin/Heidelberg, 2006).

  3. X. Yin, J. Han, J. Yang and P. S. Yu, “CrossMine: Efficient Classification across Multiple Database Relations, in Jean-Francois Boulicaut, Luc de Raedt, and Heikki Mannila (eds.), Constraint-Based Mining and Inductive Databases, Springer-Verlag LNAI vol. 3848, pp. 172-195, 2006.

  4. Jiawei Han, Benjamin W. Wah, Vijay Raghavan,Xindong Wu, and Rajeev Rastogi (eds.), Proceedings of the Fifth Int. Conf. on Data Mining (ICDM-2005), (Houston, Texas, Nov. 27--30, 2005) IEEE Computer Society, New York, 2005. (846 + xxvii pages).

  5. J. Yang, X. Yan, J. Han, and W. Wang, “Discovering Evolutionary Classifier over High Speed Non-Static Stream”, in S. Bandyopadhyay et al. (eds.),� Advanced Methods for Knowledge Discovery from Complex Data, Springer Verlag, 2005.

  6. J. Han, J. Pei, and X. Yan, “Sequential Pattern Mining by Pattern-Growth: Principles and Extensions”, in W. W. Chu and T. Y. Lin (eds.), Recent Advances in Data Mining and Granular Computing (Mathematical Aspects of Knowledge Discovery), Springer Verlag, 2005.

  7. P. Bajcsy, J. Han, L, Liu, J. Yang, “Survey of Bio-Data Analysis from Data Mining Perspective,”� Chapter 2 of D. Shasha, et al. (eds.), Data Mining in Bioinformatics, Springer Verlag, 2005, pp. 9-39.

  8. H. Yu, A. Doan, and J. Han, ``Mining for Information Discovery on the Web: Overview and Illustrative Research,” N. Zhongand J. Liu (eds.), Intelligent Technologies forInformationAnalysis}, Springer Verlag, 2004, pp. 131-163.

9. Giannella, J. Han, J. Pei, X. Yan and P.S. Yu, ``Mining Frequent Patterns in Data Streams at Multiple Time Granularities'', H. Kargupta, A. Joshi, K. Sivakumar, and Y. Yesha (eds.), Next Generation Data Mining, AAAI/MIT Press, 2004, pp.105-124.

Refereed Conference Publications (Refereed Workshop Publications are omitted due to limited space)

  1. Chao Liu and Jiawei Han, “Failure Proximity: A Fault Localization-Based Approach”, Proc. 14th ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE'06), Portland, OR, Nov. 2006.
  2. Hector Gonzalez, Jiawei Han, and Xiaolei Li, “Mining Compressed Commodity Workflows From Massive RFID Data Sets”, in Proc. 2006 Int. Conf. on Information and Knowledge Management (CIKM'06), Arlington, VA, Nov. 2006.
  3. Xiaoxin Yin, Jiawei Han, and Philip Yu, “LinkClus: Efficient Clustering via Heterogeneous Semantic Links”, in Proc. 2006 Int. Conf. on Very Large Data Bases (VLDB'06), Seoul,Korea, Sept. 2006.
  4. Hector Gonzalez, Jiawei Han, and Xiaolei Li, “FlowCube: Constructuing RFIDFlowCubes for Multi-Dimensional Analysis of Commodity Flows”, in Proc. 2006 Int. Conf. on Very Large Data Bases (VLDB'06), Seoul,Korea, Sept. 2006.
  5. Dong Xin, Chen Chen, and Jiawei Han,� “Towards Robust Indexing for Ranked Queries”, in Proc. 2006 Int. Conf. on Very Large Data Bases (VLDB'06), Seoul,Korea, Sept. 2006.
  6. Dong Xin,Jiawei Han, Hong Cheng, and Xiaolei Li, “Answering Top-k Queries with Multi-Dimensional Selections: The Ranking Cube Approach”, in Proc. 2006 Int. Conf. on Very Large Data Bases (VLDB'06), Seoul, Korea, Sept. 2006.
  7. Dong Xin, Hong Cheng, Xifeng Yan, and Jiawei Han, “Extracting Redundancy-Aware Top-K Patterns”, in Proc. 2006 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'06), Philadelphia, PA, Aug. 2006.
  8. Qiaozhu Mei, Dong Xin, Hong Cheng, ChengXiang Zhai, and Jiawei Han, “Generating Semantic Annotations for Frequent Patterns with Context Analysis”, in Proc. 2006 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'06), Philadelphia, PA, Aug. 2006.
  9. Chao Liu, Chen Chen, Jiawei Han, and Philip Yu, “GPLAG: Detection of Software Plagiarism by Procedure Dependency Graph Analysis”, in Proc. 2006 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'06), Philadelphia,PA, Aug. 2006.
  10. Dong Xin,Xuehua Shen, Qiaozhu Mei, and Jiawei Han, “Discovering Interesting Patterns Through User's Interactive Feedback”, in Proc. 2006 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'06), Philadelphia, PA, Aug. 2006.
  11. Deng Cai,Xiaofei He and Jiawei Han, “Tensor Space Model for Document Analysis”, in Proc. 2006 Int. ACM SIGIR Conf. on Research & Development on Information Retrieval (SIGIR'06), Seattle, WA, Aug. 2006.
  12. Hongyan Liu, Ying Lu, Jiawei Han, and Jun He, “Error-Adaptive and Time-Aware Maintenance of Frequency Counts over Data Streams”, in Proc. 2006 Int. Conf. on Web-Age Information Management (WAIM'06), Hong Kong, China, June, 2006.
  13. Kaushik Chakrabarti,Venkatesh Ganti, Jiawei Han, and Dong Xin, “Ranking Objects Based on Relationships”, in Proc. 2006 ACM SIGMOD Int. Conf. on Management of Data (SIGMOD'06), Chicago, IL, June 2006.
  14. Xiaolei Li, Jiawei Han, and Sangkyum Kim, “Motion-Alert: Automatic Anomaly Detection in Massive Moving Objects”, Proc. 2006 IEEE Int. Conf. on Intelligence and Security Informatics (ISI'06), San Diego, CA, May 2006.
  15. Wen Jin, Anthony K. H. Tung, Jiawei Han, and Wei Wang, “Ranking Outliers Using Symmetric Neighborhood Relationship,” in Proc. 2006 Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD'06), Singapore, April 2006.
  16. Hongyan Liu, Jiawei Han, Dong Xin, and Zheng Shao, “Mining Interesting Patterns from Very High Dimensional Data: A Top-Down Row Enumeration Approach,” in Proc. 2006 SIAM Int. Conf. on Data Mining (SDM'06), Bethesda,MD, April 2006.
  17. Chao Liu, Xifeng Yan, and Jiawei Han, “Mining Control Flow Abnormality for Logic Error Isolation,” in Proc. 2006 SIAM Int. Conf. on Data Mining (SDM'06), Bethesda, MD, April 2006.
  18. Charu Aggarwal, Chen Chen, and Jiawei Han, “On the Inverse Classification Problem and its Applications”, in Proc. 2006 Int. Conf. on Data Engineering (ICDE'06), Atlanta, Georgia, April 2006.
  19. Hector Gonzalez,� Jiawei Han, Xiaolei Li, and Diego Klabjan, “Warehousing and Analysis of Massive RFID Data Sets”, in Proc. 2006 Int. Conf. on Data Engineering (ICDE'06), Atlanta,Georgia, April 2006.
  20. Hongyan Liu, Jiawei Han, Dong Xin, and Zheng Shao, “Top-Down Mining of Interesting Patterns from Very High Dimensional Data”, in Proc. 2006 Int. Conf. on Data Engineering (ICDE'06), Atlanta, Georgia, April 2006.
  21. Dong Xin,Jiawei Han, Zheng Shao, and Hongyan Liu, “C-Cubing: Efficient Computation of Closed Cubes by Aggregation-Based Checking”, in Proc. 2006 Int. Conf. on Data Engineering (ICDE'06), Atlanta, Georgia, April 2006.
  22. Xifeng Yan,Feida Zhu, Jiawei Han, and Philip Yu, “Searching Substructures with Superimposed Distance”, in Proc. 2006 Int. Conf. on Data Engineering (ICDE'06), Atlanta, Georgia, April 2006.
  23. Deng Cai,Zheng Shao, Xiaofei He, Xifeng Yan, Jiawei Han, “Community Mining from Multi-Relational Networks”, in Proc. 2005 European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'05), Porto, Portugal, Oct., 2005.
  24. Wen Jin, Martin Ester and Jiawei Han, “Efficient Processing of Ranked Queries with Sweeping Selection”,� in Proc. 2005 European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'05), Porto, Portugal, Oct., 2005.
  25. Xiaoxin Yin and Jiawei Han, “Efficient Classification from Multiple Heterogeneous Databases”, in Proc. 2005 European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'05), Porto, Portugal, Oct., 2005.
  26. C. Liu, X. Yan, L. Fei, J. Han, and S. Midkiff, “SOBER: Statistical Model-based Bug Localization”, Proc. 2005 ACM SIGSOFT Symp. on the Foundations of Software Engineering (FSE 2005), Lisbon, Portugal, Sept. 2005.
  27. D. Xin, J. Han, X. Yan and H. Cheng, “Mining Compressed Frequent-Pattern Sets”, Proc. 2005 Int. Conf. on Very Large Data Bases (VLDB'05), Trondheim,Norway, Aug. 2005.
  28. X. Yan, H. Cheng, J. Han, and D. Xin, “Summarizing Itemset Patterns: A Profile-Based Approach”, Proc. 2005 Int. Conf. on Knowledge Discovery and Data Mining (KDD'05), Chicago, IL, Aug. 2005.
  29. X. Yan, X. J. Zhou, and J. Han, “Mining Closed Relational Graphs with Connectivity Constraints”, Proc. 2005 Int. Conf. on Knowledge Discovery and Data Mining (KDD'05), Chicago, IL, Aug. 2005.
  30. X. Yin, J. Han, and P.S. Yu, “Cross-Relational Clustering with User's Guidance”, Proc. 2005 Int. Conf. on Knowledge Discovery and Data Mining (KDD'05), Chicago, IL, Aug. 2005.
  31. S. Cong, J. Han, and D. Padua, “Parallel Mining of Closed Sequential Patterns”, Proc. 2005 Int. Conf. on Knowledge Discovery and Data Mining (KDD'05), Chicago, IL, Aug. 2005.
  32. D. Cai and X. He. “Orthogonal Locality Preserving Indexing”, Proc. 2005 Int. Conf. on Research and Development in Information Retrieval (SIGIR'05), Salvador, Brazil, Aug. 2005.
  33. X. Yin, J. Han, and J. Yang, “Searching for Related Objects in Relational Databases”, Proc. 2005 Int. Conf. on Scientific and Statistical Database Management (SSDBM'05), Santa Barbara, CA, June 2005.
  34. H. Hu, X. Yan, Yu, J. Han and X. J. Zhou, “Mining Coherent Dense Subgraphs across Massive Biological Networks for Functional Discovery”, Proc. 2005 Int. Conf. on Intelligent Systems for Molecular Biology (ISMB 2005), Ann Arbor, MI, June 2005.
  35. X. Yan, P. S. Yu, and J. Han, “Substructure Similarity Search in Graph Databases”, Proc. 2005 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD'05), Baltimore, Maryland, June 2005, pp. 766-777.
  36. C. Liu, X. Yan, H. Yu, J. Han, and P. S. Yu, “Mining Behavior Graphs for Backtrace of Noncrashing Bugs”, Proc. 2005 SIAM Int. Conf. on Data Mining (SDM'05), Newport Beach, CA, April 2005.
  37. H. Cheng, X. Yan, and J. Han, “SeqIndex: Indexing Sequences by Sequential Pattern Analysis”, Proc. 2005 SIAM Int. Conf. on Data Mining (SDM'05), Newport Beach, CA, April 2005.
  38. X. Li, J. Han, X. Yin, and D. Xin, “Mining Evolving Customer-Product Relationships in Multi-Dimensional Space”, Proc. 2005 Int. Conf. on Data Engineering (ICDE'05), Tokyo, Japan, April 2005, pp. 580-581.
  39. X. Yan, X. J. Zhou, J. Han, “Mining Closed Relational Graphs with Connectivity Constraints”, Proc. 2005 Int. Conf. on Data Engineering (ICDE'05), Tokyo, Japan, April 2005, pp. 357-358.
  40. W. Jin, J. Han, and M. Ester, “Mining Thick Skylines over Large Databases”, Proc. 2004 European Conf. on Principles of Principles and Practice of Knowledge Discovery in Databases (PKDD’04), Pisa, Italy, Sept. 2004, pp. 255-266.
  41. C. Aggarwal,� J. Han,�J. Wang,� and� P. S. Yu, “A Framework for Projected Clustering of High Dimensional Data Streams”, Proc. 2004 Int. Conf. on Very Large Data Bases (VLDB'04), Toronto, Canada, Aug. 2004.
  42. X. Li, J. Han, and H. Gonzalez, “High-Dimensional OLAP: A Minimal Cubing Approach”, Proc. 2004 Int. Conf. on Very Large Data Bases (VLDB'04), Toronto,Canada, Aug. 2004.
  43. C. Aggarwal, J. Han, J. Wang, and P. S. Yu, “On Demand Classification of Data Streams”, Proc. 2004 Int. Conf. on Knowledge Discovery and Data Mining (KDD'04), Seattle, WA, Aug. 2004.
  44. H. Cheng,� X. Yan,� and J. Han,� “IncSpan: Incremental�Mining of Sequential Patterns in Large Database”, Proc. 2004 Int. Conf. on Knowledge Discovery and Data Mining (KDD'04), Seattle, WA, Aug. 2004.
  45. B. He, K.C.-C. Chang, and J. Han, “Discovering Complex Matchings across Web Query Interfaces: A Correlation Mining Approach”, Proc.� 2004 Int. Conf. on Knowledge Discovery and Data Mining (KDD'04), Seattle,WA, Aug. 2004.
  46. Y. Li, J. Han, and� J. Yang, “Clustering Moving Objects”, Proc. 2004 Int. Conf. on Knowledge Discovery and Data Mining (KDD'04), Seattle, WA, Aug. 2004.
  47. Wu, M. Garland, and J. Han, “Mining Scale-Free Networks using Geodesic Clustering”, Proc. 2004 Int. Conf. on Knowledge Discovery and Data Mining (KDD'04), Seattle, WA, Aug. 2004.
  48. J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U.� Dayal, and M.-C. Hsu, “Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach”, IEEE Transactions on Knowledge and Data Engineering, 16(10), 2004.
  49. Z. Shao, J. Han, and D. Xin, “MM-Cubing: Computing Iceberg Cubes by Factorizing the Lattice Space”, Proc. 2004 Int. Conf. on Scientific and Statistical Database Management (SSDBM'04), Santorini Island, Greece, June 2004.
  50. Y. Li, J. Yang, and J. Han, “Continuous K-Nearest Neighbor Search for Moving Objects”, Proc. 2004 Int. Conf. on Scientific and Statistical Database Management (SSDBM'04), Santorini Island, Greece, June 2004.
  51. J. Han, J. Pei, Y. Yin and R. Mao, “Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach”, Data Mining and Knowledge Discovery, 8(1):53-87, 2004.
  52. X. Yan, P. S. Yu, and J. Han, “Graph Indexing: A Frequent Structure-based Approach”, Proc. 2004 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD'04), Paris, France, June 2004.
  53. Y. D. Cai, D. Clutter, G. Pape, J. Han, M. Welge, and L. Auvil, “MAIDS: Mining Alarming Incidents from Data Streams”, (system demonstration), Proc. 2004 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'04), Paris, France, June 2004.
  54. W.-Y. Kim, Y.-K. Lee, and J. Han, “CCMine: Efficient Mining of Confidence-Closed Correlated Patterns”, Proc. 2004 Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD'04), Sydney, Australia, May 2004.
  55. H.Yu, J. Han, K. C.-C. Chang, “PEBL:Web PageClassification Without Negative Examples”, IEEE Transactions onKnowledge and Data Engineering (Special Issue on Mining and Searching the Web),16(1): 70-81, 2004.
  56. G. Dong, J. Han, J. Lam, J. Pei, K. Wang, and W. Zou, “MiningConstrained Gradients in Multi-Dimensional Databases”, IEEE Transactions on Knowledge and Data Engineering, 16(6), 2004.
  57. X. Yin, J. Han, J. Yang, and P. S. Yu, “CrossMine: Efficient Classification across Multiple Database Relations”, Proc. 2004 Int. Conf. on Data Engineering (ICDE'04), Boston, MA, March 2004.
  58. J. Wang and J. Han, “BIDE: Efficient Mining of Frequent Closed Sequences”, Proc. 2004 Int. Conf. on Data Engineering (ICDE'04), Boston,MA, March 2004.
  59. P. Tzvetkov, X. Yan, and J. Han, “TSP: Mining Top-K Closed Sequential Patterns”,� Proc.� 2003 Int. Conf. on Data Mining (ICDM'03), Melbourne, FL, Nov. 2003.
  60. Y.-K. Lee, W.-Y. Kim, Y. D. Cai, and J. Han, “CoMine: Efficient Mining of Correlated Patterns”,� Proc.� 2003 Int. Conf. on Data Mining (ICDM'03), Melbourne, FL, Nov. 2003.
  61. Aggarwal, J. Han, J. Wang, and P. S. Yu, “A Framework for Clustering Evolving Data Streams”,� Proc.�2003 Int. Conf. on Very Large Data Bases (VLDB'03), Berlin, Germany, Sept. 2003.
  62. D. Xin, J. Han, X. Li, and B. W. Wah, “Star-Cubing: Computing Iceberg Cubes by Top-Down and Bottom-Up Integration”, Proc.� 2003 Int. Conf. on Very Large Data Bases (VLDB'03), Berlin,Germany, Sept. 2003.
  63. X. Yan and J. Han, “CloseGraph: Mining Closed Frequent Graph Patterns”, Proc. 2003 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'03), Washington, D.C., Aug. 2003..
  64. H. Yu, J. Yang, and J. Han, “Classifying Large Data Sets Using SVM with Hierarchical Clusters”, Proc. 2003 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'03), Washington, D.C., Aug. 2003.����������������������������
  65. J. Wang, J. Han, and J. Pei, “CLOSET+: Searching for the Best Strategies for Mining Frequent Closed Itemsets”, Proc. 2003 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'03), Washington, D.C., Aug. 2003.
  66. H. Wang, W. Fan, P. S. Yu, and J. Han, “Mining Concept-Drifting Data Streams using Ensemble Classifiers”, Proc. 2003 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'03), Washington, D.C., Aug. 2003.�������������������
  67. S.-J. Ko and J. Han, ``Mining the Typical Preference of Collaborative User Group'', Proc. 2003 Int. Conf. on Conceptual Modeling (ER'03), Skokie,IL, Oct. 2003.
  68. H. Yu, J. Yang, W. Wang, and J. Han, ``Discovering Compact and Highly Discriminative Features or Feature Combinations of Drug Activities Using Support Vector Machine'', Proc. 2003 IEEE Computer Society Bioinformatics Conf. (CSB'03), Stanford, California, Aug. 2003.

Project Impact

� Education: Parts of the new research results are used in Data Mining courses (CS412, CS512) for both undergraduate and graduate students being taught in the Department of Computer Science, the University of Illinoisat Urbana-Champaign. Moreover, the research results will be published timely in international conferences and journals and be distributed world-wide for education and research. The new progress will also be integrated into the new edition of my data mining textbook and other research collections.

� Collaborations: For this project we have established collaborations with IBM T.J. Watson Research Center, Microsoft Research, and NCSA (National Center of Supercomputer Applications). Through such collaborations we expect to have access to real datasets and applications and produce more research results.

Current and Future Activities

The following are some of the highlights of our ongoing work.� Please refer to the section: Publications and Products section for related references

� Development of multi-dimensional stream data analysis techniques

� Development of efficient and effective methods for mining sequential patterns in data streams

� Development of efficient methods for mining frequent patterns

� Development of efficient and scalable methods for classifications in evolving data streams

� Development of efficient and scalable methods for clustering evolving data streams

Area Background

This project is based on the previous works on data mining, stream data/query processing,and stream data mining. There have been many research papers published on these themes. Several textbooks provide good overviews of data mining principles and algorithms, including (Han and Kamber, 2006), (Hand, Mannila, and Smyth, 2001) and (Hastie, Tibshirani, and Friedman, 2001). � For stream data processing, (Babcock, et al. 2002) gives a comprehensive survey. � There have been a few previous studies on stream data mining, including (Aggarwal, et al. 2003), (Chen et al. 2002), (Hulten et al 2001), (Domingos and Hulten2000), (Manku and Motwani2002), (O'Callaghan et al 2002), and (Wang et al. 2003).

Area References

[1] C. Aggarwal, J. Han, J. Wang, and P. S. Yu. A Framework for Clustering Evolving Data Streams. VLDB 2003.

[2] B. Babcock, S. Babu, M. Datar, R. Motwani, and J. Widom. Models and issues in data stream systems. POD 2002.
[3] Y. Chen, G. Dong, J. Han, B. W. Wah, and J. Wang, Multi-Dimensional Regression Analysis of Time-Series Data Streams, VLDB 2002.

[4] A. Dobra, M. Garofalakis, J. Gehrke, and R. Rastogi. Processing Complex Aggregate Queries over Data Streams.SIGMOD 2002.

[5]M. Greenwald, S. Khanna. Space-Efficient Online Computation of QuantileSummaries. SIGMOD 2001.
[6] J. Han and M. Kamber.Data Mining: Concepts and Techniques, MorganKaufmann Publishers, 2001.
[7] D. J. Hand, H. Mannila, and P. Smyth. Principles of Data Mining, MIT Press, 2001.

[8]T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag 2001.

[8] G. Hulten, L. Spencer, and P. Domingos. Mining time-changing data streams. KDD 2001.

[9] G. Manku and R. Motwani. Approximate frequency counts over data streams. VLDB 2002.
[10] L. O'Callaghan, N. Mishra, A. Meyerson, S. Guha, and R. Motwani. High-performance clustering of streams and large data sets. ICDE 2002.

[11] H. Wang, W. Fan, P. S. Yu, and J. Han. Mining Concept-Drifting Data Streams using Ensemble Classifiers. KDD 2003.

Potential Related Projects

This project is related to most of data mining and stream data processing projects. In particularly, it is related to P.I.'s NSF IIS 020-9199 (Mining Sequential and Structured Patterns: Scalability, Flexibility, Extensibility and Applicability), and P.I.'s ONR project, MAIDS (Mining Alarming Incidents in Data Streams). We wish to collaborate or exchange research ideas with most of the research projects related to knowledge discovery in databases, stream data processing, and their applications, such as homeland security, computer networks, etc.

Project Web site URL: http://www.cs.uiuc.edu/~hanj/projs/streamine.htm

Online software: Online software related to this project can be downloaded at www.illimine.cs.uiuc.edu

Online resources: Research publications related to this project can be downloaded at Selected Publications(http://www.cs.uiuc.edu/~hanj/pubs/pub.htm)