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The reading materials were selected to help you form a basic understanding. In other words, those materials do not necessarily represent the state-of-the-art of the area. If you are interested in frontier research, it may be helpful to take a look on some major journals or conferences related to data mining, such as:

Major journals in data mining: DMKD, TKDD, TKDE, KAIS

Major journals in machine learning: MLJ, JMLR

Major journals in database: TODS, VLDBJ

Major journals in information retrieval: TOIS, IP&M, IRJ

Major conferences in web search and mining: WWWJ, TOIT, TWeb

Major conferences in data mining: KDD, ICDM, SDM, ECMLPKDD, PAKDD

Major conferences in machine learning: ICML, NIPS, COLT

Major conferences in database: SIGMOD, VLDB, ICDE

Major conferences in information retrieval: SIGIR, CIKM

Major conferences in web search and mining: WWW, WSDM

Attn: The reading materials below are listed for the course. Please do not duplicate for commercial purposes without the permission of corresponding copyright holders.

• S. Brin, R. Motwani, and C. Silverstein. Beyond market baskets: Generalizing association rules to correlations. In: Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data (SIGMOD'97), Tucson, AZ, 1997, 265-276.
• A. Ceglar and J. F. Roddick. Association mining. ACM Computing Surveys, 2006, 38(2), Article 5.
• A. A. Freitas. Understanding the crucial differences between classification and discovery of association rules - A position paper. SIGKDD Explorations, 2000, 2(1): 65-69.
• J. Han, H. Cheng, D. Xin, and X. Yan. Frequent pattern mining: Current status and future directions. Data Mining and Knowledge Discovery, 2007, 15(1): 55-86.
• 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, 2004, 8(1): 53-87.
• J. Hipp, U. Güntzer, and G. Nakhaeizadeh. Algorithms for association rule mining - A general survey and comparison. SIGKDD Explorations, 2000, 2(1): 58-64.
• P. K. Novak, N. Lavrac, and G. I. Webb. Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining. Journal of Machine Learning Research, 2009, 10: 377-403.
• J. F. Roddick and M. Spiliopoulou. A survey of temporal knowledge discovery paradigms and methods. IEEE Transactions on Knowledge and Data Engineering, 2002, 14(4): 750-767.
• M. J. Zaki. Parallel and distributed association mining: A survey. IEEE Concurrency, 1999, 7(4): 14-25.
• Q. Zhao and S. S. Bhowmick. Sequential pattern mining: A survey. Technical Report 2003118, CAIS, Nanyang Technological University, Singapore, 2003.