doi: 10.4304/jsw.6.5.873-879
An Improved Fuzzy C-means Clustering Algorithm based on PSO
- School of Computer Science &Technology China University of Mining & Technology Xuzhou Jiangsu, China
Abstract
To deal with the problem of premature convergence of the fuzzy c-means clustering algorithm based on particle swarm optimization, which is sensitive to noise and less effective when handling the data set that dimensions greater than the number of samples, a novel fuzzy c-means clustering method based on the enhanced Particle Swarm Optimization algorithm is presented. Firstly, this approach distributes the memberships on the basis of the distance between the sample and cluster centers, making memberships meet the constraints of FCM. Then, optimization strategy is presented that the optimal particle can be guided to close the group effectively. The experimental results show the proposed method significantly improves the clustering effect of the PSO-based FCM that encoded in membership.
Keywords
- clustering
- particle swarm algorithm
- fu
How to Cite
Qiang Niu and Xinjian Huang, "An Improved Fuzzy C-means Clustering Algorithm based on PSO," Journal of Software, vol. 6, no. 5, pp. 873-879, 2011. https://doi.org/ 10.4304/jsw.6.5.873-879
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).