Recommended Readings

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Association Rule Part I

· R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. SIGMOD, 207-216, 1993.

· R. Agrawal and R. Srikant. Fast algorithms for mining association rules. VLDB, 487-499, 1994.

· S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic itemset counting and implication rules for market basket analysis. SIGMOD, 255-264, 1997.

· J.S. Park, M.S. Chen, and P.S. Yu. An effective hash-based algorithm for mining association rules. SIGMOD, 175-186, 1995.

· A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. VLDB, 432-444, 1995.

· H. Toivonen. Sampling large databases for association rules. VLDB, 134-145, 1996.

Association Rule Part II

· R. J. Bayardo. Efficiently mining long patterns from databases. SIGMOD, 85-93, 1998.

· N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. ICDT, 398-416, 1999.

· J. Pei, J. Han, and R. Mao. CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets. DMKD, 11-20, 2000.

Association Rule Part III

· R. Agarwal, C. Aggarwal, and V. V. V. Prasad. A tree projection algorithm for generation of frequent itemsets. Parallel and Distributed Computing, 61(3):350-371, 2001.

· J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. SIGMOD, 1-12, 2000.

Sequential Pattern Mining I

· R. Srikant and R. Agrawal. Mining sequential patterns: Generalizations and performance improvements. EDBT, 3-17, 1996.

· J. Pei, J. Han, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu. PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. ICDE, 215-224, 2001.

· Q. Zhao, S. Bhowmick. Sequential Pattern Mining: A Survey, 2006

Clustering Part I

· P. Berkhin. Survey of clustering data mining techniques, 2002.

Clustering Part II

· R. Ng and J. Han. Efficient and effective clustering method for spatial data mining. VLDB, 144-155, 1994.

· T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH : an efficient data clustering method for very large databases. SIGMOD, 103-114, 1996.

· S. Guha, R. Rastogi, and K. Shim. Cure: an efficient clustering algorithm for large databases. SIGMOD, 73-84, 1998.

· M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases. KDD, 226-231, 1996.

Clustering Part III

· W. Wang, J. Yang, and R. Muntz. STING: a statistical information grid approach to spatial data mining. VLDB, 186-195, 1997.

· G. Sheikholeslami, S. Chatterjee, and A. Zhang. WaveCluster: a multi-resolution clustering approach for very large spatial databases. VLDB, 428-439, 1998.

· R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining. SIGMOD, 94-105, 1998.

Classification Part I

· S. K. Murthy. Automatic construction of decision trees from data: A multi-disciplinary survey, data mining and knowledge discovery. KDD Journal, 2(4), 345-389, 1998.

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Classification Part II

· http://en.wikipedia.org/wiki/Bayesian_network

· P. Spirtes and C. Glymour, An algorithm for fast recovery of sparse causal graphs, Social Science Computer Review, Vol. 9, pp. 62-72, 1991

· Pearl, Judea, Causality: Models, Reasoning, and Inference. Cambridge University Press. ISBN 0-521-77362-8. 2000

Classification Part III

· C. J. C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2(2), 121-168, 1998.

· B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining. KDD, 1998.