Supplementary Readings

Besides recommended textbook, please also refer to Resource of CS591 (Advanced Seminar on Data Mining)

Recommended readings related to each chapter

Chapter 1

Chapter 2

1. D. Barbará et al. The New Jersey Data Reduction Report.Bulletin of the Technical Committee on Data Engineering, 20, Dec. 1997, pp. 3-45.

Chapter 3

· S. Chaudhuri, and U. Dayal. An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26(1):65-74, 1997.

· J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow, and H. Pirahesh. Data cube: A relational aggregation operator generalizing group-by, cross-tab and sub-totals. Data Mining and Knowledge Discovery, 1(1):29-54, 1997.

Chapter 4

1. Y. Zhao, P. M. Deshpande, and J. F. Naughton. An array-based algorithm for simultaneous multidimensional aggregates. In SIGMOD'97, pp. 159-170, Tucson, Arizona, May 1997.

2. K. Beyer and R. Ramakrishnan. Bottom-up computation of sparse and iceberg cubes. In SIGMOD'99, pp. 359--370, Philadelphia, PA, June 1999.

3. 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.

4. J. Han, J. Pei, G. Dong, and K. Wang, '' Efficient Computation of Iceberg Cubes with Complex Measures '', Proc. 2001 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD'01), Santa Barbara, CA, May 2001.

Chapter 5

· R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In VLDB'94, pp. 487-499, Santiago, Chile, Sept. 1994.

· J. Han, J. Pei, and Y. Yin, '' Mining Frequent Patterns without Candidate Generation'', Proc. 2000 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD'00), Dallas, TX, May 2000.

· Tianyi Wu, Yuguo Chen and Jiawei Han, “Association Mining in Large Databases: A Re-Examination of Its Measures”, in Proc. 2007 Int. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'07), Warsaw, Poland, Sept. 2007.

· Feida Zhu, Xifeng Yan, Jiawei Han, and Philip S. Yu, “gPrune: A Constraint Pushing Framework for Graph Pattern Mining”, in Proc. 2007 Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD'07), Nanjing, China, May 2007.

· Feida Zhu, Xifeng Yan, Jiawei Han, Philip S. Yu, and Hong Cheng, “Mining Colossal Frequent Patterns by Core Pattern Fusion”, in Proc. 2007 Int. Conf. on Data Engineering (ICDE'07), Istanbul, Turkey, April 2007.

Chapter 6

1. Hong Cheng, Xifeng Yan, Jiawei Han, and Chih-Wei Hsu, “Discriminative Frequent Pattern Analysis for Effective Classification”, in Proc. 2007 Int. Conf. on Data Engineering (ICDE'07), Istanbul, Turkey, April 2007.

2. Hong Cheng, Xifeng Yan, Jiawei Han, and Philip S. Yu, "Direct Discriminative Pattern Mining for Effective Classification", Proc. 2008 Int. Conf. on Data Engineering (ICDE'08), Cancun, Mexico, April 2008.

Chapter 7

1. H. Wang, W. Wang, J. Yang, and P.S. Yu. Clustering by pattern similarity in large data sets, Proc. the ACM SIGMOD International Conference on Management of Data (SIGMOD), Madison, Wisconsin, 2002.

2. X. Yin, J. Han, and P.S. Yu, “Cross-Relational Clustering with User's Guidance”, in Proc. 2005 Int. Conf. on Knowledge Discovery and Data Mining (KDD'05), Chicago, IL, Aug. 2005

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.