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    Please use this identifier to cite or link to this item: http://ir.lib.ksu.edu.tw/handle/987654321/5810

    Title: Mountain C-Regressions in Comparing Fuzzy C-Regressions
    Authors: Miin-Shen Yang
    Kuo-Lung Wu (吳國龍)
    June-Nan Hsieh
    Keywords: Fuzzy clustering
    Fuzzy c-means
    Switching regressions
    Fuzzy c-regressions
    Mountain clustering
    Mountain c-regressions
    Date: 2007
    Issue Date: 2009-11-18 11:49:47 (UTC+8)
    Abstract: In fuzzy clustering, the fuzzy c-mean (FCM) is a most used algorithm. The embedding of FCM into switching regressions, called the fuzzy c-regression (FCR), was proposed by Hathaway and Bezdek in 1993. However, these FCRs always heavily depend on the initial values. In this paper, we propose a mountain c-regressions (MCR) to solve the initial-value problem where the MCR is based on the modified mountain clustering method. We first transform the data set into a parameter space and then take a random sample from the transformed data set. We implement the modified mountain clustering on the random samples for switching regressions. The proposed MCR can form c estimated regression models for switching regression data sets without giving initials. Several examples show the accuracy and effectiveness of the proposed MCR method.
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