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Soft Computing

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01.03.2011 | Original Paper

An application of particle swarm optimization algorithm to clustering analysis

verfasst von: R. J. Kuo, M. J. Wang, T. W. Huang

Erschienen in: Soft Computing | Ausgabe 3/2011

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Abstract

Particle swarm optimization algorithm (PSOA), which maintains a population of particles, where each particle represents a potential solution to an optimization problem, is a population-based stochastic search process. This study intends to integrate PSOA with K-means to cluster data. It is shown that PSOA can be employed to find the centroids of a user-specified number of clusters. The proposed PSOA is evaluated using four data sets, and compared to the performance of some other PSOA-based methods and K-means method. Computational results show that the proposed method has much potential. A real-world problem for order clustering also illustrates that the proposed method is quite promising.

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Titel: An application of particle swarm optimization algorithm to clustering analysis
verfasst von: R. J. Kuo
M. J. Wang
T. W. Huang
Publikationsdatum: 01.03.2011
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 3/2011
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-009-0539-5

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