Effective Fuzzy Ontology Based Distributed Document Using Non-Dominated Ranked Genetic Algorithm

The increase in the number of documents has aggravated the difficulty of classifying those documents according to specific needs. Clustering analysis in a distributed environment is a thrust area in artificial intelligence and data mining. Its fundamental task is to utilize characters to compute the degree of related corresponding relationship between objects and to accomplish automatic classification without earlier knowledge. Document clustering utilizes clustering technique to gather the documents of high resemblance collectively by computing the documents resemblance. Recent studies have shown that ontologies are useful in improving the performance of document clustering. Ontology is concerned with the conceptualization of a domain into an individual identifiable format and machine-readable format containing entities, attributes, relationships, and axioms. By analyzing types of techniques for document clustering, a better clustering technique depending on Genetic Algorithm (GA) is determined. Non-Dominated Ranked Genetic Algorithm (NRGA) is used in this paper for clustering, which has the capability of providing a better classification result. The experiment is conducted in 20 newsgroups data set for evaluating the proposed technique. The result shows that the proposed approach is very effective in clustering the documents in the distributed environment.