Integration of K-means algorithm and AprioriSome algorithm for fuzzy sequential pattern mining

Authors:
R. J. Kuo;C. M. Chao;C. Y. Liu
Affiliations:
Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Section 4, Kee-Lung Road, Taipei 106, Taiwan, ROC;Department of Business Management, National Taipei University of Technology, No. 1, Section 3, Chung-Hsiao East Road, Taipei 106, Taiwan, ROC;Department of Industrial Engineering and Management, National Taipei University of Technology, No. 1, Section 3, Chung-Hsiao East Road, Taipei 106, Taiwan, ROC
Venue:
Applied Soft Computing
Year:
2009

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Abstract

Since Agrawal and Srikant proposed sequential pattern mining in 1995, there have been many scholars working to improve the efficiency and reduce the processing time of algorithms. This study intends to propose a fuzzy AprioriSome algorithm for fuzzy sequential patterns mining with integration with clustering technique, K-means algorithm. Two experiments performed using transaction data provided by a securities firm and foodmarket data from SQL sever 2000 demonstrate the strength of fuzzy AprioriSome sequential pattern mining in mining large quantity of transaction data.