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TEC
2008

Genetic-Fuzzy Data Mining With Divide-and-Conquer Strategy

13 years 11 months ago
Genetic-Fuzzy Data Mining With Divide-and-Conquer Strategy
Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transaction data 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. A genetic algorithm (GA)-based framework for finding membership functions suitable for mining problems is proposed. The fitness of each set of membership functions is evaluated by the fuzzy-supports of the linguistic terms in the large 1-itemsets and by the suitability of the derived membership functions. The evaluation by the fuzzy supports of large 1-itemsets is much faster than that when considering all itemsets or interesting association rules. It can also help divide-and-conquer the derivation process of the membership functions for different items. The proposed GA framework, thus...
Tzung-Pei Hong, Chun-Hao Chen, Yeong-Chyi Lee, Yu-
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TEC
Authors Tzung-Pei Hong, Chun-Hao Chen, Yeong-Chyi Lee, Yu-Lung Wu
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