Applied Mathematics & Information Sciences
The availability of large quantity of text documents from theWorldWideWeb 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 practical 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 containing entities, attributes, relationships and axioms. By analyzing all types of techniques for document clustering, a clustering technique depending 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.
Thangamani, M. and Thangaraj, P.
"Fuzzy Ontology for Distributed Document Clustering based on Genetic Algorithm,"
Applied Mathematics & Information Sciences: Vol. 07:
4, Article 42.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol07/iss4/42