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

• Published in: 2009 World Congress on Nature and Biologically Inspired Computing pp. 1690 - 1694
• Main Authors: Izakian, H., Abraham, A., Snasel, V.
• Format: Conference Proceeding
• Language: English
• 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
• DOI: 10.1109/NABIC.2009.5393618
• ISBN: 1424450535; 9781424450534

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.