A GA-based Fuzzy Mining Approach to Achieve a Trade-off Between Number of Rules and Suitability of Membership Functions

Authors:
Tzung-Pei Hong;Chun-Hao Chen;Yu-Lung Wu;Yeong-Chyi Lee
Affiliations:
Department of Electrical Engineering, National University of Kaohsiung, 811, Kaohsiung, Taiwan, R.O.C;Department of Computer Science and Information Engineering, National Cheng-Kung University, 701, Tainan, Taiwan, R.O.C;Institute of Information Management, I-Shou University, 840, Kaohsiung, Taiwan, R.O.C;Institute of Information Engineering, I-Shou University, 840, Kaohsiung, Taiwan, R.O.C
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Year:
2006

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Abstract

Data mining is most commonly used in attempts to induce association rules from transaction data. Transactions in real-world applications, however, usually consist of quantitative values. This paper thus proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. We present a GA-based framework for finding membership functions suitable for mining problems and then use the final best set of membership functions to mine fuzzy association rules. The fitness of each chromosome is evaluated by the number of large 1-itemsets generated from part of the previously proposed fuzzy mining algorithm and by the suitability of the membership functions. Experimental results also show the effectiveness of the framework.