Abstract
A new Fuzzy c-Means Clustering Algorithm based on Particle Swarm Optimization (PSOFCM) is presented after analyzing the advantages and disadvantages of the classical fuzzy c-means clustering algorithm. It avoids the local optima, and also is robust to initialization. The fluctuation however has appeared in the new algorithm, so the improved PSOFCM has been proposed finally which has better convergence to lower quantization errors. We compared the performance of PSOFCM, improved PSOFCM and FCM with IRIS testing data. The experiments show that the performance of improved PSOFCM is far better than FCM and this is a viable and effective clustering algorithm.
| Original language | English |
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| Title of host publication | Proceedings of the World Congress on Intelligent Control and Automation (WCICA) |
| Pages | 6055-6058 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2006 |
| Event | 6th World Congress on Intelligent Control and Automation, WCICA 2006 - Dalian, China Duration: 21 Jun 2006 → 23 Jun 2006 |
Publication series
| Name | Proceedings of the World Congress on Intelligent Control and Automation (WCICA) |
|---|---|
| Volume | 2 |
Conference
| Conference | 6th World Congress on Intelligent Control and Automation, WCICA 2006 |
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| Country/Territory | China |
| City | Dalian |
| Period | 21/06/06 → 23/06/06 |
Keywords
- Fuzzy c-means
- PSO
- PSOFCM
- Swarm intelligence
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Wang, L., Liu, Y., Zhao, X., & Xu, Y. (2006). Particle swarm optimization for Fuzzy c-Means Clustering. In Proceedings of the World Congress on Intelligent Control and Automation (WCICA) (pp. 6055-6058). Article 1714243 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 2). https://doi.org/10.1109/WCICA.2006.1714243