DataminingConceptsandteniques6.ppt

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Ref: Basic Concepts of Frequent Pattern Mining (Association Rules) R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. SIGMOD93. (Max-pattern) R. J. Bayardo. Efficiently mining long patterns from databases. SIGMOD98. (Closed-pattern) N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. ICDT99. (Sequential pattern) R. Agrawal and R. Srikant. Mining sequential patterns. ICDE95 * Ref: Apriori and Its Improvements R. Agrawal and R. Srikant. Fast algorithms for mining association rules. VLDB94. H. Mannila, H. Toivonen, and A. I. Verkamo. Efficient algorithms for discovering association rules. KDD94. A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. VLDB95. J. S. Park, M. S. Chen, and P. S. Yu. An effective hash-based algorithm for mining association rules. SIGMOD95. H. Toivonen. Sampling large databases for association rules. VLDB96. S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic itemset counting and implication rules for market basket analysis. SIGMOD97. S. Sarawagi, S. Thomas, and R. Agrawal. Integrating association rule mining with relational database systems: Alternatives and implications. SIGMOD98. * Ref: Depth-First, Projection-Based FP Mining R. Agarwal, C. Aggarwal, and V. V. V. Prasad. A tree projection algorithm for generation of frequent itemsets. J. Parallel and Distributed Computing:02. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. SIGMOD’ 00. J. Liu, Y. Pan, K. Wang, and J. Han. Mining Frequent Item Sets by Opportunistic Projection. KDD02. J. Han, J. Wang, Y. Lu, and P. Tzvetkov. Mining Top-K Frequent Closed Patterns without Minimum Support. ICDM02. J. Wang, J. Han, and J. Pei. CLOSET+: Searching for the Best Strategies for Mining Frequent