Web Documents Clustering Using PSO Algorithm and Ontology

Abstract

The largest shared information source, the World Wide Web has been increasing a tremendous proliferation in the amount of information rapidly. As a result of its huge sharing and highly dynamic data, there is a need for grouping the documents into clusters for faster information retrieval. Clustering web documents is collection of documents into groups such that the documents within each group are similar to each other. Document clustering is one of the main challenging tasks in web data mining and it is still requires an efficient clustering techniques. The typical way of representing a web document is a huge box of terms. The representation of these terms is often unsatisfactory as it does not exploit the semantics. This paper proposes Particle swarm Optimization (PSO) and Ontology for clustering of text documents. A domain Ontology is developed thus it enriched with semantic and synonyms for the representation of terms. Particle Swarm Optimization (PSO) algorithm is used to cluster high voluminous of data efficiently. This paper presents comparative results of using PSO algorithm only and PSO algorithm using Ontology for clustering web documents. The analysis result of test data is efficient enough in representation of terms by using Ontology and the performance of PSO clustering algorithm is high in intra cluster and inters cluster similarity.