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 language | English |
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Title of host publication | 2nd International Conference on Digital Image Processing |
DOIs | |
Publication status | Published - 2010 |
Externally published | Yes |
Event | International Conference on Digital Image Processing (ICDIP) 2010 - Singapore, Singapore Duration: 26 Feb 2010 → 28 Feb 2010 Conference number: 2nd https://www.spiedigitallibrary.org/conference-proceedings-of-spie/7546.toc (Proceedings) |
Publication series
Name | Proceedings of SPIE - The International Society for Optical Engineering |
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Volume | 7546 |
ISSN (Print) | 0277-786X |
Conference
Conference | International Conference on Digital Image Processing (ICDIP) 2010 |
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Abbreviated title | ICDIP 2010 |
Country/Territory | Singapore |
City | Singapore |
Period | 26/02/10 → 28/02/10 |
Internet address |
Keywords
- Image segmentation
- K-means
- Particle swarm optimisation
- Supervised learning