K-Optimal Rule Discovery

K-optimal rule discovery finds the K rules that optimize a user-specified measure of rule value with respect to a set of sample data and user-specified constraints. This approach avoids many limitations of the frequent itemset approach of association rule discovery. This paper presents a scalable algorithm applicable to a wide range of K-optimal rule discovery tasks and demonstrates its efficiency.

[1]  森下 真一,et al.  Parallel Branch-and-Bound Graph Search for Correlated Association Rules , 1999 .

[2]  Ron Kohavi,et al.  Real world performance of association rule algorithms , 2001, KDD '01.

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

[4]  Dimitrios Gunopulos,et al.  Constraint-Based Rule Mining in Large, Dense Databases , 2004, Data Mining and Knowledge Discovery.

[5]  Carla E. Brodley,et al.  KDD-Cup 2000 organizers' report: peeling the onion , 2000, SKDD.

[6]  Hannu Toivonen,et al.  Sampling Large Databases for Association Rules , 1996, VLDB.

[7]  Nicolas Pasquier,et al.  Discovering Frequent Closed Itemsets for Association Rules , 1999, ICDT.

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

[9]  Foster J. Provost,et al.  RL4: a tool for knowledge-based induction , 1990, [1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence.

[10]  Peter Clark,et al.  The CN2 Induction Algorithm , 1989, Machine Learning.

[11]  Shinichi Morishita,et al.  Parallel Branch-and-Bound Graph Search for Correlated Association Rules , 1999, Large-Scale Parallel Data Mining.

[12]  Gregory Piatetsky-Shapiro,et al.  Discovery, Analysis, and Presentation of Strong Rules , 1991, Knowledge Discovery in Databases.

[13]  Ramesh C Agarwal,et al.  Depth first generation of long patterns , 2000, KDD '00.

[14]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[15]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[16]  Edith Cohen,et al.  Finding Interesting Associations without Support Pruning , 2001, IEEE Trans. Knowl. Data Eng..

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

[18]  Roberto J. Bayardo,et al.  Mining the most interesting rules , 1999, KDD '99.

[19]  Geoffrey I. Webb.  Efficient search for association rules , 2000, KDD '00.

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

[21]  Jian Pei,et al.  CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets , 2000, ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

[22]  Bruce G. Buchanan,et al.  Dendral and Meta-Dendral: Their Applications Dimension , 1978, Artif. Intell..

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

[24]  N. Lavra,et al.  Predictive Performance of Weighted Relative Accuracy , 2000 .

[25]  Peter A. Flach,et al.  Predictive Performance of Weghted Relative Accuracy , 2000, PKDD.

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

[27]  Roberto J. Bayardo,et al.  Efficiently mining long patterns from databases , 1998, SIGMOD '98.

[28]  Geoffrey I. Webb.  OPUS: An Efficient Admissible Algorithm for Unordered Search , 1995, J. Artif. Intell. Res..

[29]  Heikki Mannila,et al.  Fast Discovery of Association Rules , 1996, Advances in Knowledge Discovery and Data Mining.

[30]  Ryszard S. Michalski,et al.  Synthesis of Optimal and Quasi-Optimal Variable-Valued Logic Formulas , 1975 .

[31]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[32]  Oren Etzioni,et al.  Learning Decision Lists Using Homogeneous Rules , 1994, AAAI.

[33]  Mohammed J. Zaki.  Generating non-redundant association rules , 2000, KDD '00.