An Improved PSO Clustering Algorithm with Entropy-Based Fuzzy Clustering

Authors: , ,

Abstract: Particle swarm optimization is a based-population heuristic global optimization technology and is referred to as a swarm-intelligence technique. In general, each particle is initialized randomly which increases the iteration time and makes the result unstable. In this paper an improved clustering algorithm combined with entropy-based fuzzy clustering (EFC) is presented. Firstly EFC algorithm gets an initial cluster center. Then the cluster center is regarded as inputs of one of all particles instead of being initialized randomly. Finally we cluster with the improved clustering algorithm which guarantees unique clustering. The experimental results show that the improved clustering algorithm has not only high accuracy but also certain stability.

WSEAS Transactions on Computers, ISSN / E-ISSN: 1109-2750 / 2224-2880, Volume 14, 2015, Art. #10

PDF
XML
Cite
×
Citation Tools
WSEAS AMA
Yuyan Zheng, Yang Zhou, Jianhua Qu, "An Improved PSO Clustering Algorithm with Entropy-Based Fuzzy Clustering," WSEAS Transactions on Computers, vol. 14, pp. 88-96, 2015, DOI:
Yuyan Zheng, Yang Zhou, Jianhua Qu. An Improved PSO Clustering Algorithm with Entropy-Based Fuzzy Clustering. WSEAS Transactions on Computers. 2015;14:88-96.
Copy to Clipboard
Citation copied to Clipboard

Certification

---