Particle Swarm Optimization Based Clustering of Web Usage Data

Abstract

Web session clustering is one of the important web usage mining techniques which aims to group usage sessions on the basis of some similarity measures. In this paper we describe a new web session clustering algorithm that uses particle swarm optimization. We review the existing web usage clustering techniques and propose a swarm intelligence based PSO-clustering algorithm for the clustering of web user sessions. The proposed algorithm works independently without hybridization with any other clustering algorithm. The results show that our approach performs better than the benchmark K-means clustering algorithm for clustering web usage sessions.

DOI: 10.1109/WIIAT.2008.292

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Cite this paper

@article{Alam2008ParticleSO, title={Particle Swarm Optimization Based Clustering of Web Usage Data}, author={Shafiq Alam and Gillian Dobbie and Patricia Riddle}, journal={2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology}, year={2008}, volume={3}, pages={451-454} }