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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8764

    Title: A new fuzzy clustering algorithms based on transformed data
    Authors: Liu, Hsiang-Chuan;Jeng, Bai-Cheng;Wu, Der-Bang;Lo, Yi-Hsiang
    Contributors: Department of Bioinformatics
    Keywords: Control theory;Copying;Cybernetics;Fuzzy clustering;Fuzzy rules;Fuzzy systems;Robot learning;Between-cluster variation;Data transformation;FCM;FCM algorithm;FCS;FCS algorithms;FTCM;Fuzzy C-means algorithms;Improved algorithm;Objective function-based clustering;Objective functions;Real data sets
    Date: 2009
    Issue Date: 2010-04-08 20:05:50 (UTC+8)
    Publisher: Asia University
    Abstract: The popular fuzzy c-means algorithm (FCM) is an objective function based clustering method. Hence, different objective function may lead to different results. The important issue is how to get a more compact and separable objective function to improve the cluster accuracy. The objective function of the well known improved algorithm, FCS, is a generalization of the FCM objective function by combining fuzzy within- and between-cluster variations. In this paper, considering a more separable data transformation, the improved new algorithm, "Fuzzy Transformed C-Mean (FTCM)", is proposed. Three real data sets were applied to prove that the performance of the FTCM algorithm is better than the conventional FCM algorithm and the FCS algorithm. © 2009 IEEE.
    Relation: Proceedings of the 2009 International Conference on Machine Learning and Cybernetics 5 :3036-3041
    Appears in Collections:[生物資訊與醫學工程學系 ] 會議論文

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