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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/107156


    Title: Mining Drift of Fuzzy Membership Functions
    Authors: Hong, Tzung-Pei;Wu, Min-Thai;Li, Yan-Kang;Chen, Chun-Hao
    Keywords: Concept drift;Data mining;Fuzzy C-means;Membership functions
    Date: 2016-03-14
    Issue Date: 2016-08-18 13:32:52 (UTC+8)
    Abstract: In this paper, the fuzzy c-means (FCM) clustering approach is adopted to find concept drift of fuzzy membership functions. The proposed algorithm is divided into two stages. In the first stage, the FCM approach is used to find appropriate fuzzy membership functions at different periods or at different places. Then in the second stage, the proposed algorithm compares the results in the first stage to find different types of drift of fuzzy membership functions. Experiments are also made to show the performance of the proposed approach.
    Relation: the series Lecture Notes in Computer Science 9622, pp.211-218
    Appears in Collections:[資訊工程學系暨研究所] 會議論文

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