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

Tzung-Pei Hong, Chun-Hao Chen, Yeong-Chyi Lee, Yu-Lung Wu
2008 IEEE Transactions on Evolutionary Computation  
Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binaryvalued 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
more » ... problems is proposed. The fitness of each set of membership functions is evaluated by the fuzzysupports of the linguistic terms in the large 1-itemsets and by the suitability of the derived membership functions. Experiments are conducted to analyze different fitness functions and setting different supports and confidences. Experiments are also conducted to compare the proposed algorithm, the one with uniform fuzzy partition, and the existing one without divide-and-conquer, with results validating the performance of the proposed algorithm.
doi:10.1109/tevc.2007.900992 fatcat:apl7s2ebgzfcrddxa6slaadaay