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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, TKDE, TKDD, 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 data mining: SIGKDD, ICDM, SDM, PKDD, PAKDD

Major conferences in machine learning: NIPS, ICML, COLT, ECML

Major conferences in database: SIGMOD, VLDB, ICDE, PODS

Major conferences in information retrieval: SIGIR, CIKM

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

• R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB'94), Santiago, Chile, 1994, 487-499.
• 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 and Y. Fu. Discovery of multiple-level association rules from large databases. In: Proceedings of the 21st International Conference on Very Large Data Bases (VLDB'95), Zurich, Switzerland, 1995, 420-431.
• 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. G¨šntzer, and G. Nakhaeizadeh. Algorithms for association rule mining - A general survey and comparison. SIGKDD Explorations, 2000, 2(1): 58-64.
• B. Lent, A. Swami, and J. Widom. Clustering association rules. In: Proceedings of the 13th IEEE International Conference on Data Engineering (ICDE'97), Birmingham, UK, 1997, 220-231.
• M. J. Zaki. Parallel and distributed association mining: A survey. IEEE Concurrency, 1999, 7(4): 14-25.