Mining Frequent Itemsets Using Support Constraints

Interesting patterns often occur at varied levels of support. The classic association mining based on a uniform minimum support, such as Apriori, either misses interesting patterns of low support or suuers from the bottleneck of itemset generation. A better solution is to exploit support constraints, which specify what minimum support is required for what itemsets, so that only necessary itemsets are generated. In this paper, we present a framework of frequent itemset mining in the presence of support constraints. Our approach is to \push" support constraints into the Apriori itemset generation so that the \best" minimum support is used for each itemset at run time to preserve the essence of Apriori.

[1]  Jinyan Li,et al.  Eecient Mining of Emerging Patterns: Discovering Trends and Diierences , 1999 .

[2]  Jinyan Li,et al.  Efficient mining of emerging patterns: discovering trends and differences , 1999, KDD '99.

[3]  Salvatore J. Stolfo,et al.  Mining Audit Data to Build Intrusion Detection Models , 1998, KDD.

[4]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[5]  Shamkant B. Navathe,et al.  An Efficient Algorithm for Mining Association Rules in Large Databases , 1995, VLDB.

[6]  Edith Cohen,et al.  Finding interesting associations without support pruning , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[7]  Wynne Hsu,et al.  Mining association rules with multiple minimum supports , 1999, KDD '99.

[8]  Rajeev Motwani,et al.  Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.

[9]  Ron Rymon,et al.  Search through Systematic Set Enumeration , 1992, KR.

[10]  Jiawei Han,et al.  Discovery of Multiple-Level Association Rules from Large Databases , 1995, VLDB.

[11]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[12]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.

[13]  Rajeev Motwani,et al.  Beyond market baskets: generalizing association rules to correlations , 1997, SIGMOD '97.

[14]  Ke Wang,et al.  Building Hierarchical Classifiers Using Class Proximity , 1999, VLDB.

[15]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

[16]  Dimitris Meretakis,et al.  Extending naïve Bayes classifiers using long itemsets , 1999, KDD '99.

[17]  Heikki Mannila,et al.  Efficient Algorithms for Discovering Association Rules , 1994, KDD Workshop.

[18]  Philip S. Yu,et al.  A new framework for itemset generation , 1998, PODS '98.

[19]  Philip S. Yu,et al.  An effective hash-based algorithm for mining association rules , 1995, SIGMOD '95.

[20]  Ramakrishnan Srikant,et al.  Mining Association Rules with Item Constraints , 1997, KDD.

[21]  Ramakrishnan Srikant,et al.  Mining generalized association rules , 1995, Future Gener. Comput. Syst..