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Flocking based approach for data clustering

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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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Authors and Affiliations

  1. Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Ave., Tehran, Iran

    Abbas Ahmadi

  2. Electrical and Computer Engineering Department, University of Waterloo, Waterloo, ON, Canada

    Fakhri Karray & Mohamed S. Kamel

Authors
  1. Abbas Ahmadi
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  2. Fakhri Karray
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  3. Mohamed S. Kamel
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Correspondence to Abbas Ahmadi.

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Ahmadi, A., Karray, F. & Kamel, M.S. Flocking based approach for data clustering. Nat Comput 9, 767–791 (2010). https://doi.org/10.1007/s11047-009-9173-5

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