Fuzzy C-means and fuzzy swarm for fuzzy clustering problem |
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| Authors: | Hesam Izakian Ajith Abraham |
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| Affiliation: | 1. Department of Computer Engineering, University Of Isfahan, Iran;2. Machine Intelligence Research Labs, MIR-Labs, Washington, USA;1. Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China;2. State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100049, China;1. MITS, Rayagada, India;2. ANITS, Vishakapatnam, India;3. CUTM, Paralakhemundi, India |
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| Abstract: | 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. |
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