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Proceedings Paper

Hybrid particle swarm optimisation for data clustering
Author(s): Sing Loong Teng; Chee Seng Chan; Mei Kuan Lim; Weng Kin Lai
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Paper 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.

Paper Details

Date Published: 26 February 2010
PDF: 6 pages
Proc. SPIE 7546, Second International Conference on Digital Image Processing, 75460E (26 February 2010); doi: 10.1117/12.852246
Show Author Affiliations
Sing Loong Teng, Univ. Malaysia Sarawak (Malaysia)
Mimos Berhad (Malaysia)
Chee Seng Chan, Mimos Berhad (Malaysia)
Mei Kuan Lim, Mimos Berhad (Malaysia)
Weng Kin Lai, Mimos Berhad (Malaysia)


Published in SPIE Proceedings Vol. 7546:
Second International Conference on Digital Image Processing

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