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Ontology-based Semantic Text Documents Clustering Using Particle Swarm Optimization

http://hdl.handle.net/20.500.12678/0000004807
ae81c32d-2eab-4622-95f6-606785e586b4
5a0d4f0a-73f8-4e4e-9088-0043df1f84be
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proceeding_total-pages-130-138.pdf proceeding_total-pages-130-138.pdf (3172 Kb)
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Article
Upload type
Publication
Title
Title Ontology-based Semantic Text Documents Clustering Using Particle Swarm Optimization
Language en
Publication date 2017-02-16
Authors
Lwin, Wai Wai
Kham, Nang Saing Moon
Description
The World Wide Web, the largest sharedinformation source is growing exponentially and theamount of business news on the web is overwhelmingand need to be handled properly. As such, grouping theweb document into cluster for speedy informationretrieved becomes imperative. Clustering techniqueorganizes a large quantity of unordered text documentsinto a small number of meaningful and coherentclusters, thereby providing a basis for intuitive andinformative navigation and browsing mechanisms. Thequality of clustering result depends greatly on therepresentation of documents and the clusteringalgorithm. In traditional document representationmethods, the frequency count of the document terms isused for the feature vector representing the documents.But traditional document representation methodscannot identify related terms semantically. Documentswritten in human language contain contexts and thewords used to describe these contexts are generallysemantically related. Motivated by this fact, domainontology is developed to promote the enrichment ofsemantic representation of terms. Then, Particle SwarmOptimization (PSO) clustering algorithm is used tocluster the web documents efficiently. The paperconstitutes the comparative results of using PSOalgorithm only and PSO algorithm with Ontology forclustering web documents. According to the analyticalresults, the representation of terms by using Ontologyis significantly efficient and the implementation of PSOalgorithm achieves better performance in intra clusterand inters cluster similarity.
Keywords
document clustering, representations, PSO, Ontology, inter cluster, intra cluster
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/703
Journal articles
Fifteenth International Conference on Computer Applications(ICCA 2017)
Conference papers
Books/reports/chapters
Thesis/dissertations
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