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Web Documents Clustering Using PSO Algorithm and Ontology

http://hdl.handle.net/20.500.12678/0000004682
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2b4a27c7-4339-48af-ab85-54c98ce5028e
None
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201655.pdf 201655.pdf (236 Kb)
Publication type
Article
Upload type
Publication
Title
Title Web Documents Clustering Using PSO Algorithm and Ontology
Language en
Publication date 2016-02-25
Authors
Lwin, Wai Wai
Kham, Nang Saing Moon
Description
The largest sharedinformation source, theWorld Wide Web hasbeen increasing atremendous proliferationin the amount ofinformation rapidly. As aresult of its huge sharingand highly dynamicdata, there is a need forgrouping the documentsinto clusters for fasterinformation retrieval.Clustering webdocuments is collectionof documents into groupssuch that the documentswithin each group aresimilar to each other.Document clustering isone of the mainchallenging tasks in webdata mining and it is stillrequires an efficientclustering techniques.The typical way ofrepresenting a webdocument is a huge boxof terms. Therepresentation of theseterms is oftenunsatisfactory as it doesnot exploit thesemantics. This paperproposes Particle swarmOptimization (PSO) andOntology for clusteringof text documents. Adomain Ontology isdeveloped thus itenriched with semanticand synonyms for therepresentation of terms.Particle SwarmOptimization (PSO)algorithm is used tocluster high voluminousof data efficiently. Thispaper presentscomparative results ofusing PSO algorithmonly and PSO algorithmusing Ontology forclustering webdocuments. The analysisresult of test data isefficient enough inrepresentation of terms by using Ontology andthe performance of PSOclustering algorithm ishigh in intra cluster andinters cluster similarity.
Keywords
document clustering, representation of terms, PSO, Ontology, inter cluster, intra cluster
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/345
Journal articles
Fourteenth International Conference On Computer Applications (ICCA 2016)
Conference papers
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Thesis/dissertations
328
150
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