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Computational Statistics & Data Analysis
A survey of Web clustering engines
ACM Computing Surveys (CSUR)
Hybridization of the ant colony optimization with the k-means algorithm for clustering
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
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Various optimization methods are used along with the standard clustering algorithms to make the clustering process simpler and quicker. In this paper we propose a new hybrid technique of clustering known as K-Evolutionary Particle Swarm Optimization (KEPSO) based on the concept of Particle Swarm Optimization (PSO). The proposed algorithm uses the K-means algorithm as the first step and the Evolutionary Particle Swarm Optimization (EPSO) algorithm as the second step to perform clustering. The experiments were performed using the clustering benchmark data. This method was compared with the standard K-means and EPSO algorithms. The results show that this method produced compact results and performed faster than other clustering algorithms. Later, the algorithm was used to cluster web pages. The web pages were clustered by first cleaning the unnecessary data and then labeling the obtained web pages to categorize them.