{"_buckets": {"deposit": "a725ca88-6f3e-4aae-bf95-480b676ac3fb"}, "_deposit": {"id": "4818", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4818"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4818", "sets": ["1597824273898", "user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Correlation Coefficient-based K-means Clustering for K-NN", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "K-nearest neighbor algorithm is one of themost popular classifications in machine learningzone. However, as k-nearest neighbor is a lazylearning method, when a system bases on hugeamount of history data, it faces processingperformance degradation. Many researchersusually care about only classification accuracy,but the speed of estimation also play an essentialrole in real time prediction systems. For this issue,this research proposes correlation coefficientbasedk-mean clustering for k-nearest neighboraiming at upgrading the performance of k-nearestneighbor classification by improving processingtime performance. For the experiments, we usedthe real data sets, Breast Cancer, Breast Tissueand Iris, from UCI machine learning repository.Moreover, the real traffic data collected fromOjana junction, Route 58, Okinawa, Japan, wasalso utilized to show the efficiency of this method.By using these datasets, we prove the betterprocessing performance and prediction accuracyof the new approach by comparing the classicalk-nearest neighbor with the new k-nearestneighbor."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value": []}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value": []}, "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": "Aung, Swe Swe"}, {"subitem_authors_fullname": "Nagayama, Itaru"}, {"subitem_authors_fullname": "Tamaki, Shiro"}]}]}, "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/123456789/719"}, "item_title": "Correlation Coefficient-based K-means Clustering for K-NN", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004818", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-11"}, "publish_date": "2019-07-11", "publish_status": "0", "recid": "4818", "relation": {}, "relation_version_is_last": true, "title": ["Correlation Coefficient-based K-means Clustering for K-NN"], "weko_shared_id": -1}