Application of particle swarm optimization to association rule mining

Authors:
R. J. Kuo;C. M. Chao;Y. T. Chiu
Affiliations:
Department of Industrial Management, National Taiwan University of Science and Technology No. 43, Section 4, Kee-Lung Road, Taipei 106, Taiwan;Department of Business Management, National Taipei University of Technology No. 1, Section 3, Chung-Hsiao East Road, Taipei 106, Taiwan;Department of Industrial Engineering and Management National Taipei University of Technology No. 1, Section 3, Chung-Hsiao East Road, Taipei 106, Taiwan
Venue:
Applied Soft Computing
Year:
2011

Citing 12
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Abstract

In the area of association rule mining, most previous research had focused on improving computational efficiency. However, determination of the threshold values of support and confidence, which seriously affect the quality of association rule mining, is still under investigation. Thus, this study intends to propose a novel algorithm for association rule mining in order to improve computational efficiency as well as to automatically determine suitable threshold values. The particle swarm optimization algorithm first searches for the optimum fitness value of each particle and then finds corresponding support and confidence as minimal threshold values after the data are transformed into binary values. The proposed method is verified by applying the FoodMart2000 database of Microsoft SQL Server 2000 and compared with a genetic algorithm. The results indicate that the particle swarm optimization algorithm really can suggest suitable threshold values and obtain quality rules. In addition, a real-world stock market database is employed to mine association rules to measure investment behavior and stock category purchasing. The computational results are also very promising.