文化大學機構典藏 CCUR:Item 987654321/2474
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    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://irlib.pccu.edu.tw/handle/987654321/2474


    题名: Competitive algorithms for the clustering of noisy data
    作者: Yang TN, Wang SD
    贡献者: 資訊科學系
    日期: 2004
    上传时间: 2009-11-02 11:29:04 (UTC+8)
    摘要: In this paper, we consider the issue of clustering when outliers exist. The outlier set is defined as the complement of the data set. Following this concept, a specially designed fuzzy membership weighted objective function is proposed and the corresponding optimal membership is derived. Unlike the membership of fuzzy c-means, the derived fuzzy membership does not reduce with the increase of the cluster number. With the suitable redefinition of the distance metric, we demonstrate that the objective function could be used to extract c spherical shells. A hard clustering algorithm alleviating the prototype under-utilization problem is also derived. Artificially generated data are used for comparisons. (C) 2002 Elsevier B.V. All rights reserved.
    關聯: FUZZY SETS AND SYSTEMS v.141 n.2 Pages: 281-299
    显示于类别:[資訊工程學系] 期刊論文

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