Fuzzy clustering using hybrid fuzzy c-means and fuzzy particle swarm optimization

Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. However FCM is sensitive to initi...

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Bibliographic Details
Published in2009 World Congress on Nature and Biologically Inspired Computing pp. 1690 - 1694
Main Authors Izakian, H., Abraham, A., Snasel, V.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2009
Subjects
Ant colony optimization
Clustering algorithms
Clustering methods
fuzzy clustering
Fuzzy sets
Iterative algorithms
Machine intelligence
Machine learning algorithms
Particle swarm optimization
Partitioning algorithms
Stochastic processes
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Summary:Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. However FCM is sensitive to initialization and is easily trapped in local optima. Particle swarm optimization (PSO) is a stochastic global optimization tool which is used in many optimization problems. In this paper a hybrid fuzzy clustering method based on FCM and fuzzy PSO (FPSO) is proposed which make use of the merits of both algorithms. Experimental results show that our proposed method is efficient and can reveal encouraging results.
ISBN:1424450535
9781424450534
DOI:10.1109/NABIC.2009.5393618