{"_buckets": {"deposit": "2b4a27c7-4339-48af-ab85-54c98ce5028e"}, "_deposit": {"id": "4682", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4682"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4682", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Web Documents Clustering Using PSO Algorithm and Ontology", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "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."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "document clustering"}, {"interim": "representation of terms"}, {"interim": "PSO"}, {"interim": "Ontology"}, {"interim": "inter cluster"}, {"interim": "intra cluster"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-07-03"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "201655.pdf", "filesize": [{"value": "236 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 236000.0, "url": {"url": "https://meral.edu.mm/record/4682/files/201655.pdf"}, "version_id": "a02f0d7f-01c0-4dfb-9cb6-85d1bd13b368"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Fourteenth International Conference On Computer Applications (ICCA 2016)", "subitem_pages": "", "subitem_volume": ""}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "", "subitem_c_date": "", "subitem_conference_title": "", "subitem_part": "", "subitem_place": "", "subitem_session": "", "subitem_website": ""}]}, "item_1583103211336": {"attribute_name": "Books/reports/chapters", "attribute_value_mlt": [{"subitem_book_title": "", "subitem_isbn": "", "subitem_pages": "", "subitem_place": "", "subitem_publisher": ""}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_awarding_university": "", "subitem_supervisor(s)": [{"subitem_supervisor": ""}]}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Lwin, Wai Wai"}, {"subitem_authors_fullname": "Kham, Nang Saing Moon"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2016-02-25"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/345"}, "item_title": "Web Documents Clustering Using PSO Algorithm and Ontology", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004682", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-03"}, "publish_date": "2019-07-03", "publish_status": "0", "recid": "4682", "relation": {}, "relation_version_is_last": true, "title": ["Web Documents Clustering Using PSO Algorithm and Ontology"], "weko_shared_id": -1}