Using divide-and-conquer GA strategy in fuzzy data mining

Authors:
Tzung-Pei Hong;Chun-Hao Chen;Yu-Lung Wu;Yeong-Chyi Lee
Affiliations:
Dept. of Electr. Eng., Nat. Univ. of Kaohsiung, Taiwan;Dept. of Inf. Sci. & Intelligent Syst., Tokushima Univ., Japan;Dept. of Inf. Sci. & Intelligent Syst., Tokushima Univ., Japan;Dept. of Inf. Sci. & Intelligent Syst., Tokushima Univ., Japan
Venue:
ISCC '04 Proceedings of the Ninth International Symposium on Computers and Communications 2004 Volume 2 (ISCC"04) - Volume 02
Year:
2004

Citing 0
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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 work thus proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. A GA-based framework for finding membership functions suitable for mining problems is proposed. The fitness of each set of membership functions is evaluated using the fuzzy-supports of the linguistic terms in the large 1-itemsets and the suitability of the derived membership functions. The proposed framework thus maintains multiple populations of membership functions, with one population for one item's membership functions. The final best set of membership functions gathered from all the populations is used to effectively mine fuzzy association rules.