{"_buckets": {"deposit": "2bb1e689-fee0-4c9e-885b-07cf75ea438e"}, "_deposit": {"id": "4830", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4830"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4830", "sets": ["1597824273898", "user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Clustering Technique based on Concept Weight to Text Documents", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Documents clustering become an essentialtechnology with the popularity of the Internet.That also means that fast and high-qualitydocument clustering technique play core topics.Text clustering or shortly clustering is aboutdiscovering semantically related groups in anunstructured collection of documents. Clusteringhas been very popular for a long time because itprovides unique ways of digesting andgeneralizing large amounts of information. Oneof the issue of clustering is to extract properfeature (terms) of a problem domain. Theexisting clustering technology mainly focuses onterm weight calculation. To achieve moreaccurate document clustering, more informativefeatures including concept weight are important.Feature Selection is important for clusteringprocess because some of the irrelevant orredundant feature may misguide the clusteringresults. To counteract this issue, the proposedsystem uses the concept weight for clustering inaccordance with the principles of ontology. To acertain extent, it has resolved the semanticproblem in specific areas."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value": []}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-07-02"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "9018.pdf", "filesize": [{"value": "53 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 53000.0, "url": {"url": "https://meral.edu.mm/record/4830/files/9018.pdf"}, "version_id": "d0062af6-dc0f-4797-9b4c-1b3e48b644a4"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Ninth International Conference On Computer Applications (ICCA 2011)", "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": "Tar, Hmway Hmway"}, {"subitem_authors_fullname": "Nyunt, Thi Thi  Soe"}]}]}, "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": "2011-05-05"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/73"}, "item_title": "Clustering Technique based on Concept Weight to Text Documents", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004830", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-02"}, "publish_date": "2019-07-02", "publish_status": "0", "recid": "4830", "relation": {}, "relation_version_is_last": true, "title": ["Clustering Technique based on Concept Weight to Text Documents"], "weko_shared_id": -1}