{"_buckets": {"deposit": "70cbc0c7-e747-495d-a906-9f0e082c8ec8"}, "_deposit": {"id": "3754", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "3754"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/3754", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Cancer Diagnosis Using K-Means Clustering", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "The proliferation, ubiquity and increasing powerof computer technology has aided data collection,processing, management and storage. However, thecaptured data needs to be converted intoinformation and knowledge to become useful. Datamining is the process of using computing power toapply methodologies, including new techniques forknowledge discovery, to data. Data mining identifiestrends within data that go beyond simple dataanalysis. Through the use of sophisticatedalgorithms, non-statistician users have theopportunity to identify key attributes of processesand target opportunities. This paper intends tosupport these non-statistician users in analysis ofthe cancer diagnosis by implementing the k-meansclustering algorithm. In this paper, the BloodCancer diagnosis is analysis in speciality."}]}, "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": "2019-07-31"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "54065.pdf", "filesize": [{"value": "287 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 287000.0, "url": {"url": "https://meral.edu.mm/record/3754/files/54065.pdf"}, "version_id": "e92272b3-b1e4-4ced-af07-e22b809afd17"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Fourth Local Conference on Parallel and Soft Computing", "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": "Mon, Aye Chan"}]}]}, "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": "2009-12-30"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/1488"}, "item_title": "Cancer Diagnosis Using K-Means Clustering", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000003754", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-31"}, "publish_date": "2019-07-31", "publish_status": "0", "recid": "3754", "relation": {}, "relation_version_is_last": true, "title": ["Cancer Diagnosis Using K-Means Clustering"], "weko_shared_id": -1}