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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/92504


    Title: Genetic-fuzzy mining with taxonomy
    Authors: Chen, Chun-Hao;Hong, Tzung-Pei;Lee, Yeong-Chyi
    Contributors: 淡江大學資訊工程學系
    Keywords: Data mining;genetic algorithm;multiple-concept levels;membership function;fuzzy association rule
    Date: 2012-10
    Issue Date: 2013-10-16 16:07:51 (UTC+8)
    Publisher: Singapore: World Scientific Publishing Co. Pte. Ltd.
    Abstract: Data mining is most commonly used in attempts to induce association rules from transaction data. Since transactions in real-world applications usually consist of quantitative values, many fuzzy association-rule mining approaches have been proposed on single- or multiple-concept levels. However, the given membership functions may have a critical influence on the final mining results. In this paper, we propose a multiple-level genetic-fuzzy mining algorithm for mining membership functions and fuzzy association rules using multiple-concept levels. It first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy. The fitness value of each individual is then evaluated by the summation of large 1-itemsets of each item in different concept levels and the suitability of membership functions in the chromosome. After the GA process terminates, a better set of multiple-level fuzzy association rules can then be expected with a more suitable set of membership functions. Experimental results on a simulation dataset also show the effectiveness of the algorithm.
    Relation: International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems 20 Suppl.2, pp.187-205
    DOI: 10.1142/S021848851240020X
    Appears in Collections:[資訊工程學系暨研究所] 期刊論文

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