{"_buckets": {"deposit": "5a0d4f0a-73f8-4e4e-9088-0043df1f84be"}, "_deposit": {"id": "4807", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4807"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4807", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Ontology-based Semantic Text Documents Clustering Using Particle Swarm Optimization", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "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."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "document clustering"}, {"interim": "representations"}, {"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": "2019-07-11"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "proceeding_total-pages-130-138.pdf", "filesize": [{"value": "3172 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 3172000.0, "url": {"url": "https://meral.edu.mm/record/4807/files/proceeding_total-pages-130-138.pdf"}, "version_id": "133cff79-f6c9-4b81-95d6-250401a9f449"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Fifteenth International Conference on Computer Applications(ICCA 2017)", "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": "2017-02-16"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/703"}, "item_title": "Ontology-based Semantic Text Documents Clustering Using Particle Swarm Optimization", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004807", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-11"}, "publish_date": "2019-07-11", "publish_status": "0", "recid": "4807", "relation": {}, "relation_version_is_last": true, "title": ["Ontology-based Semantic Text Documents Clustering Using Particle Swarm Optimization"], "weko_shared_id": -1}