Fuzzy C-means and fuzzy swarm for fuzzy clustering problem

• Published in: Expert systems with applications Vol. 38; no. 3; pp. 1835 - 1838
• Main Authors: Izakian, Hesam, Abraham, Ajith
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2011
• Subjects: Algorithms; Clustering; Expert systems; Fuzzy; Fuzzy clustering; Fuzzy logic; Fuzzy set theory; Optimization; Particle swarm optimization; Particle swarm optimization; Fuzzy clustering
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2010.07.112

► Fuzzy c-means is sensitive to initialization and is easily trapped in local optima. ► Fuzzy Particle swarm optimization can find more efficient results in some cases. ► Hybrid fuzzy c-means and fuzzy particle swarm optimization can achieve the best results. 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.