Hybrid particle swarm optimisation for data clustering

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

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1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2nd International Conference on Digital Image Processing
DOIs
Publication statusPublished - 2010
Externally publishedYes
EventInternational Conference on Digital Image Processing (ICDIP) 2010 - Singapore, Singapore
Duration: 26 Feb 201028 Feb 2010
Conference number: 2nd
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/7546.toc (Proceedings)

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume7546
ISSN (Print)0277-786X

Conference

ConferenceInternational Conference on Digital Image Processing (ICDIP) 2010
Abbreviated titleICDIP 2010
Country/TerritorySingapore
CitySingapore
Period26/02/1028/02/10
Internet address

Keywords

  • Image segmentation
  • K-means
  • Particle swarm optimisation
  • Supervised learning

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