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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/38703

    Title: K-means-based fuzzy classifier design
    Authors: 翁慶昌;Wong, Ching-chang;Chen, Chia-chong;Yeh, Shih-liang
    Contributors: 淡江大學電機工程學系
    Date: 2000-05-07
    Issue Date: 2010-04-15 11:35:38 (UTC+8)
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Abstract: In this paper, a method based on the K-means algorithm is proposed to efficiently design a fuzzy classifier so that the training patterns can be correctly classified by the proposed approach. In this method, the K-means algorithm is first used to partition the training data for each class into several clusters, and the cluster center and the radius for each cluster are calculated. Then, a fuzzy system design method that uses a fuzzy rule to represent a cluster is proposed such that a fuzzy classifier can be efficiently constructed to correctly classify the training data. The proposed method has the following features: 1) it does not need prior parameter definition; 2) it only needs a short training time; and 3) it is simple. Finally, two examples are used to illustrate and examine the proposed method for the fuzzy classifier design
    Relation: Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference on (Volume:1 ), pp.48-52
    DOI: 10.1109/FUZZY.2000.838632
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Proceeding

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