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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/96216

    Authors: Chen, Chun-Hao;Hong, Tzung-Pei;Tseng, Vincent S.;Chen, Lien-Chin
    Contributors: 淡江大學資訊工程學系
    Keywords: Multi-objective optimization;Genetic algorithm;Fuzzy set;Fuzzy association rules;Data mining
    Date: 2012-10
    Issue Date: 2014-03-06 13:47:08 (UTC+8)
    Publisher: Kumamoto: I C I C International
    Abstract: Many approaches have been proposed for mining fuzzy association rules.The membership functions, which critically influence the final mining results, are difficult to define. In general, multiple criteria are considered when defining membership functions. In this paper, a multi-objective genetic-fuzzy mining algorithm is proposed for extracting membership functions and association rules from quantitative transactions.Two objective functions are used to find the Pareto front. The first one is the suitability of membership functions. It consists of the coverage factor and the overlap factor and is used to avoid two unsuitable types of membership function. The second one is the total
    number of large 1-itemsets from a given set of minimum support values. Experimental results show the effectiveness of the proposed approach in finding the Pareto-front membership functions.
    Relation: International Journal of Innovative Computing, Information and Control 8(10A), pp.6551-6568
    Appears in Collections:[資訊工程學系暨研究所] 期刊論文

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