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Ontology-based Semantic Text Documents Clustering Using Particle Swarm Optimization

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dc.contributor.author Lwin, Wai Wai
dc.contributor.author Kham, Nang Saing Moon
dc.date.accessioned 2019-07-11T04:02:30Z
dc.date.available 2019-07-11T04:02:30Z
dc.date.issued 2017-02-16
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/703
dc.description.abstract The World Wide Web, the largest shared information source is growing exponentially and the amount of business news on the web is overwhelming and need to be handled properly. As such, grouping the web document into cluster for speedy information retrieved becomes imperative. Clustering technique organizes a large quantity of unordered text documents into a small number of meaningful and coherent clusters, thereby providing a basis for intuitive and informative navigation and browsing mechanisms. The quality of clustering result depends greatly on the representation of documents and the clustering algorithm. In traditional document representation methods, the frequency count of the document terms is used for the feature vector representing the documents. But traditional document representation methods cannot identify related terms semantically. Documents written in human language contain contexts and the words used to describe these contexts are generally semantically related. Motivated by this fact, domain ontology is developed to promote the enrichment of semantic representation of terms. Then, Particle Swarm Optimization (PSO) clustering algorithm is used to cluster the web documents efficiently. The paper constitutes the comparative results of using PSO algorithm only and PSO algorithm with Ontology for clustering web documents. According to the analytical results, the representation of terms by using Ontology is significantly efficient and the implementation of PSO algorithm achieves better performance in intra cluster and inters cluster similarity. en_US
dc.language.iso en en_US
dc.publisher Fifteenth International Conference on Computer Applications(ICCA 2017) en_US
dc.subject document clustering en_US
dc.subject representations en_US
dc.subject PSO en_US
dc.subject Ontology en_US
dc.subject inter cluster en_US
dc.subject intra cluster en_US
dc.title Ontology-based Semantic Text Documents Clustering Using Particle Swarm Optimization en_US
dc.type Article en_US

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