Hybrid particle swarm optimisation for data clustering

Sing Loong Teng; Chee Seng Chan; Mei Kuan Lim; Weng Kin Lai

doi:10.1117/12.852246

26 February 2010 Hybrid particle swarm optimisation for data clustering

Sing Loong Teng, Chee Seng Chan, Mei Kuan Lim, Weng Kin Lai

Proceedings Volume 7546, Second International Conference on Digital Image Processing; 75460E (2010) https://doi.org/10.1117/12.852246
Event: Second International Conference on Digital Image Processing, 2010, Singapore, Singapore

Abstract

Finding a best clustering algorithm to tackle the problem of finding the optimal partition of a data set is always an NP-hard problem. In general, solutions to the NP-hard problems involve searches through vast spaces of possible solutions and evolutionary algorithms have been a success. In this paper, we explore one such approach which is hardly known outside the search heuristic field - the Particle Swarm Optimisation+k-means (PSOk) for this purpose. The proposed hybrid algorithm consists of two modules, the PSO module and the k-means module. For the initial stage, the PSO module is executed for a short period to search for the clusters centroid locations. Succeeding to the PSO module is the refining stage where the detected locations are transferred to the k-means module for refinement and generation of the final optimal clustering solution. Experimental results on two challenging datasets and a comparison with other hybrid PSO methods has demonstrated and validated the effectiveness of the proposed solution in terms of precision and computational complexity.

Citation Download Citation

Sing Loong Teng, Chee Seng Chan, Mei Kuan Lim, and Weng Kin Lai "Hybrid particle swarm optimisation for data clustering", Proc. SPIE 7546, Second International Conference on Digital Image Processing, 75460E (26 February 2010); https://doi.org/10.1117/12.852246

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