Flocking based approach for data clustering |
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Authors: | Abbas Ahmadi Fakhri Karray Mohamed S Kamel |
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Affiliation: | (1) Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Ave., Tehran, Iran;(2) Electrical and Computer Engineering Department, University of Waterloo, Waterloo, ON, Canada |
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Abstract: | Data clustering is a process of extracting similar groups of the underlying data whose labels are hidden. This paper describes
different approaches for solving data clustering problem. Particle swarm optimization (PSO) has been recently used to address
clustering task. An overview of PSO-based clustering approaches is presented in this paper. These approaches mimic the behavior
of biological swarms seeking food located in different places. Best locations for finding food are in dense areas and in regions
far enough from others. PSO-based clustering approaches are evaluated using different data sets. Experimental results indicate
that these approaches outperform K-means, K-harmonic means, and fuzzy c-means clustering algorithms. |
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Keywords: | |
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