Automatic high-dimensional association rule generation for large relational data sets

Data mining extracts knowledge from a large amount of data. It has been used in a variety of applications ranging from business and marketing to bioinformatics and genomics. Many data mining algorithms currently available, however, generate relatively simple rules that include a small number of attributes. Moreover, these algorithms need to build decision trees, which take a significant amount of time due to a large number of attributes and lack of field knowledge. Thus, in this paper, we propose a method that automatically generates high-dimensional association rules in large data sets with high accuracy and broad coverage.

[1]  Definition of distributions , 2004 .

[2]  Nils J. Nilsson,et al.  Artificial Intelligence: A New Synthesis , 1997 .

[3]  Edward H. Shortliffe,et al.  The application of certainty factors to neural computing for rule discovery , 2000, IEEE Trans. Neural Networks Learn. Syst..

[4]  Lawrence J. Mazlack,et al.  Causal possibility model structures , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

[5]  Fazilah Haron,et al.  Time series prediction using adaptive association rules , 2005, First International Conference on Distributed Frameworks for Multimedia Applications.

[6]  Shiwei Tang,et al.  Association Rule Mining Based on the Multiple-Dimensional Item Attributes , 2004, ER.

[7]  J. R. Quinlan.  Learning Logical Definitions from Relations , 1990 .

[8]  Rakesh Agrawal,et al.  SPRINT: A Scalable Parallel Classifier for Data Mining , 1996, VLDB.

[9]  Tomasz Imielinski,et al.  An Interval Classifier for Database Mining Applications , 1992, VLDB.

[10]  Gregory F. Cooper,et al.  A Simple Constraint-Based Algorithm for Efficiently Mining Observational Databases for Causal Relationships , 1997, Data Mining and Knowledge Discovery.

[11]  W. H. Inmon,et al.  Understanding Data Pattern Processing: The Key to Competitive Advantage , 1991 .

[12]  Johannes Fürnkranz,et al.  Incremental Reduced Error Pruning , 1994, ICML.

[13]  Jiawei Han,et al.  Knowledge Discovery in Databases: An Attribute-Oriented Approach , 1992, VLDB.

[14]  Baowen Xu,et al.  A prediction method of fuzzy association rules , 2003, Proceedings Fifth IEEE Workshop on Mobile Computing Systems and Applications.

[15]  Rajeev Motwani,et al.  Scalable Techniques for Mining Causal Structures , 1998, Data Mining and Knowledge Discovery.

[16]  Y. P. Singh,et al.  Evolutionary approach to data mining , 2000, Proceedings of IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No.00TH8482).

[17]  Keith C. C. Chan,et al.  Fuzzy data mining for discovering changes in association rules over time , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).