@inproceedings{37f818b9c004453296d573cf00bfee97,
title = "Image clustering using Particle Swarm Optimization",
abstract = "This paper proposes an image clustering algorithm using Particle Swarm Optimization (PSO) with two improved fitness functions. The PSO clustering algorithm can be used to find centroids of a user specified number of clusters. Two new fitness functions are proposed in this paper. The PSO-based image clustering algorithm with the proposed fitness functions is compared to the K-means clustering. Experimental results show that the PSO-based image clustering approach, using the improved fitness functions, can perform better than K-means by generating more compact clusters and larger inter-cluster separation.",
keywords = "K-means clustering, image clustering, particle swarm optimization, partitional clustering",
author = "Wong, \{Man To\} and Xiangjian He and Yeh, \{Wei Chang\}",
year = "2011",
doi = "10.1109/CEC.2011.5949627",
language = "English",
isbn = "9781424478347",
series = "2011 IEEE Congress of Evolutionary Computation, CEC 2011",
publisher = "IEEE Computer Society",
pages = "262--268",
booktitle = "2011 IEEE Congress of Evolutionary Computation, CEC 2011",
address = "United States",
}