Fuzzy C-mean clustering algorithms based on picard iteration and particle swarm optimization

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Accession number:20094612445045

Title: Fuzzy C-mean clustering algorithms based on picard iteration and particle swarm optimization

Authors: Liu, Hsiang-Chuan (1); Yih, Jeng-Ming (2); Wu, Der-Bang (3);

Liu, Shin-Wu (4)

Author affiliation:(1) Department of Bioinformatics, Asia University, Taichung, 41354, Taiwan; (2) Graduate Institute of Educational Measurement; (3) Department of Mathematics Education, Taichung University, Taichung, 40306, Taiwan; (4) Department of Cell and Developmental Biology, Rutgers University, United States

Corresponding author:Liu, H.-C.

([email protected])

Source title:2008 International Workshop on Education Technology and Training and 2008 International Workshop on Geoscience and Remote Sensing, ETT and GRS 2008

Abbreviated source title:Int. Workshop Educ. Technol. Train. Int.

Workshop Geosci. Remote Sens., ETT GRS Volume:2

Monograph title:2008 International Workshop on Education Technology and Training and 2008 International Workshop on Geoscience and Remote Sensing, ETT and GRS 2008

Issue date:2009

Publication year:2009 Pages:838-842

Article number:5070490 Language:English

ISBN-13:9780769535630

Document type:Conference article (CA)

Conference name:2008 International Workshop on Education Technology and Training and 2008 International Workshop on Geoscience and Remote Sensing, ETT and GRS 2008

Conference date:December 21, 2008 - December 22, 2008 Conference location:Shanghai, China

Conference code:78327

Sponsor:Institute of Electrical and Electronics Engineers; IEEE Circuits and Systems Society; Intell. Inf. Technol. Appl. Res. Assoc.;

International Symposium on Intelligent; Information Technology

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Application

Publisher:IEEE Computer Society, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States

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

2008 IEEE.

Number of references:11

Main heading:Clustering algorithms

Controlled terms: Copying - Fuzzy clustering - Fuzzy rules - Fuzzy systems - Geology - Particle swarm optimization (PSO) - Remote sensing - Technical presentations

Uncontrolled terms: 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

Classification code:921.5 Optimization Techniques - 921.4

Combinatorial Mathematics, Includes Graph Theory, Set Theory - 903.2 Information Dissemination - 903.1 Information Sources and Analysis - 901.2 Education - 961 Systems Science - 745.2

Reproduction, Copying - 723.4 Artificial Intelligence - 723 Computer Software, Data Handling and Applications - 721 Computer Circuits and Logic Elements - 481.1 Geology - 731.1 Control Systems

DOI:10.1109/ETTandGRS.2008.375 Database:Compendex

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