{"id":203643,"updated":"2025-01-19T20:29:42.427321+00:00","links":{},"created":"2025-01-19T01:05:59.636058+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00203643","sets":["1164:2735:10153:10154"]},"path":["10154"],"owner":"44499","recid":"203643","title":["セルベースのDBSCANのAnytimeアルゴリズムの提案と性能評価"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-02-24"},"_buckets":{"deposit":"86878a95-fb05-4568-8f95-8f111fe9d806"},"_deposit":{"id":"203643","pid":{"type":"depid","value":"203643","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"セルベースのDBSCANのAnytimeアルゴリズムの提案と性能評価","author_link":["502641","502642","502643","502640"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"セルベースのDBSCANのAnytimeアルゴリズムの提案と性能評価"}]},"item_type_id":"4","publish_date":"2020-02-24","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"島根大学学術研究院理工学系"},{"subitem_text_value":"広島市立大学大学院情報科学研究科"},{"subitem_text_value":"広島工業大学情報学部"},{"subitem_text_value":"広島市立大学大学院情報科学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Institute of Science and Engineering, Academic Assembly, Shimane University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Sciences, Hiroshima City University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Informatics, Hiroshima Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Sciences, Hiroshima City University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/203643/files/IPSJ-MPS20127007.pdf","label":"IPSJ-MPS20127007.pdf"},"date":[{"dateType":"Available","dateValue":"2022-02-24"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS20127007.pdf","filesize":[{"value":"1.4 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"7cb5ce89-1ceb-442b-894a-5c23a647d146","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2020 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"酒井, 達弘"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"田村, 慶一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"北上, 始"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"竹澤, 寿幸"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10505667","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8833","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"ビッグデータへの注目の高まりにより，データクラスタリング手法の高速化が求められている．本論文では，一定の精度のクラスタリング結果を高速に算出でき，最終的には厳密解を算出可能なセルベースの DBSCAN の Anytime アルゴリズムを提案する．DBSCAN は密度をクラスタリングの基準とした代表的なデータクラスタリング手法の一つである．DBSCAN は計算コストの大きいことが知られており，その高速化手法としてセルベースの DBSCAN が提案されている．セルベースの DBSCAN はデータセット全体をセルごとに分割し，密度をセル単位で考え，セルを結合していくことでクラスタリングを行う．提案するセルベースの DBSCAN の Anytime アルゴリズムは，ランダムに選ばれた一部のセルの結合を行い，高速にクラスタリング結果を算出する．そして，セルの結合とクラスタリング結果の算出を繰り返し行う．提案手法が算出するクラスタリング結果は，処理が進むに連れて高精度となり，最終的には厳密解となる．評価実験の結果，提案手法は既存手法と比較して高精度なクラスタリング結果を高速に算出できることを示した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2020-02-24","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"7","bibliographicVolumeNumber":"2020-MPS-127"}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}