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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/106387

    Title: A GA-based approach for mining membership functions and concept-drift patterns
    Authors: Chen, C. H.;Li, Y.;Hong, T. P.;Li, Y. K.;Lu, E. H. C.
    Keywords: concept drift;data mining;fuzzy association rules;genetic algorithms;membership functions
    Date: 2015-05-25
    Issue Date: 2016-04-27 11:11:30 (UTC+8)
    Publisher: IEEE
    Abstract: Since customers' behaviors may change over time in real applications, algorithms that can be utilized to mine these drift patterns are needed. In this paper, we propose a GA-based approach for mining fuzzy concept-drift patterns. It consists of two phases. The first phase mines membership functions and the second one finds fuzzy concept-drift patterns. In the first phase, appropriate membership functions for items are derived by GA with a designed fitness function. Then, the derived membership functions are utilized to mine fuzzy concept-drift patterns in the second phase. Experiments on simulated datasets are also made to show the effectiveness of the proposed approach.
    Relation: Evolutionary Computation (CEC), 2015 IEEE, pp.2961-2965
    DOI: 10.1109/CEC.2015.7257257
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Proceeding

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