Fuzzy Ontology for Distributed Document Clustering based on Genetic Algorithm
M. Thangamani, P. Thangaraj · Applied Mathematics & Information Sciences · 2013
The availability of large quantity of text documents from the World Wide Web and business document management systems has made the dynamic separation of texts into new categories as a very important task for every business intelligence systems. But, present text clustering algorithms still suffer from problems of practica l applicability. Recent studies have shown that, in order to improve the performance of document clustering, ontologies are useful. Ontology is nothing but the conceptualization of a domain into an individual identifiable format, but machine-readable format contain ing entities, attributes, relationships and axioms. By analyzing all types of techniques for document clustering, a clustering technique d epending on Genetic Algorithm (GA) is determined to be better as GA is a global convergence technique and has the ability of determining the most suitable cluster centers without difficulties. In this paper, a new document clustering scheme with fuzzy ontology based genetic clustering is proposed. The experimental results reveal that the proposed approach increases the accuracy to a large extent and the clustering time is also highly reduced.