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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8778


    Title: Fuzzy C-mean clustering algorithms based on picard iteration and particle swarm optimization
    Authors: Liu, Hsiang-Chuan;Yih, Jeng-Ming;Wu, Der-Bang;Liu, Shin-Wu
    Contributors: Department of Bioinformatics
    Keywords: Copying;Fuzzy clustering;Fuzzy rules;Fuzzy systems;Geology;Particle swarm optimization (PSO);Remote sensing;Technical presentations;FCM algorithm;Fitness evaluations;Fuzzy C mean;Fuzzy c-mean clustering algorithm;Fuzzy C-means algorithms;Local minimums;Objective functions;Optimization problems;Picard iteration;Real data sets;Real-world application;Robust strategy
    Date: 2009
    Issue Date: 2010-04-08 20:06:01 (UTC+8)
    Publisher: Asia University
    Abstract: The popular fuzzy c-means algorithm (FCM) converges to a local minimum of the objective function. Hence, different initializations may lead to different results. The important issue is how to avoid getting a bad local minimum value to improve the cluster accuracy. The particle swarm optimization (PSO) is a popular and robust strategy for optimization problems. But the main difficulty in applying PSO to real-world applications is that PSO usually need a large number of fitness evaluations before a satisfying result can be obtained. In this paper, the improved new algorithm, Fuzzy C-Mean based on Picard iteration and PSO (PPSO-FCM)", is proposed. Two real data sets were applied to prove that the performance of the PPSO-FCM algorithm is better than the conventional FCM algorithm and the PSO-FCM algorithm. © 2008 IEEE.
    Relation: 2008 International Workshop on Education Technology and Training and 2008 International Workshop on Geoscience and Remote Sensing, ETT and GRS 2008 2:838-842
    Appears in Collections:[生物資訊與醫學工程學系 ] 會議論文

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