{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00223471","sets":["934:1022:11101:11102"]},"path":["11102"],"owner":"44499","recid":"223471","title":["時系列データの自動ネットワーク構造検出アルゴリズム"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-01-13"},"_buckets":{"deposit":"f98cb87f-def0-4572-9de0-3de2e686aab8"},"_deposit":{"id":"223471","pid":{"type":"depid","value":"223471","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"時系列データの自動ネットワーク構造検出アルゴリズム","author_link":["588543","588540","588542","588539","588537","588538","588544","588536","588535","588541"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"時系列データの自動ネットワーク構造検出アルゴリズム"},{"subitem_title":"Automatic Network Structure-based Clustering of Multivariate Time Series","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[研究論文] 時系列データ，ネットワーク構造，グラフィカルラッソ","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2023-01-13","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"大阪大学産業科学研究所産業科学AIセンター／大阪大学情報科学研究科"},{"subitem_text_value":"大阪大学産業科学研究所産業科学AIセンター"},{"subitem_text_value":"大阪大学産業科学研究所産業科学AIセンター"},{"subitem_text_value":"大阪大学産業科学研究所産業科学AIセンター／大阪大学情報科学研究科"},{"subitem_text_value":"大阪大学産業科学研究所産業科学AIセンター"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"SANKEN, Osaka University / IST, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"SANKEN, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"SANKEN, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"SANKEN, Osaka University / IST, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"SANKEN, Osaka 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/223471/files/IPSJ-TOD1601002.pdf","label":"IPSJ-TOD1601002.pdf"},"date":[{"dateType":"Available","dateValue":"2025-01-13"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TOD1601002.pdf","filesize":[{"value":"8.7 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保志"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kohei, Obata","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yasuko, Matsubara","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Koki, Kawabata","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kota, Nakamura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yasushi, Sakurai","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11464847","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_3_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7799","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本論文では，ネットワーク構造を持つ多次元時系列データのためのパターン検出手法であるNGLについて述べる．NGLは，時間変化するネットワーク構造を持つ多次元時系列データが与えられたときに，その時系列データの中から重要なネットワーク構造を発見し，それらの情報を要約，表現する．具体的に，提案手法は，(a)多次元時系列データからネットワーク構造に基づいた解釈性の高いクラスタを発見する．(b)その際に最適な分割点とクラスタ数を自動的に決定する．すなわち，事前情報の付与が必要ない．そして，(c)自動決定アルゴリズムにより高精度なクラスタリングを実現する．人工データを用いた精度評価実験では最新の既存手法と比較して提案手法が大幅な精度向上を達成していることを明らかにした．また，実データを用いた実験ではNGLが解釈性の高いクラスタを発見していることを確認した．","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In this paper we present NGL, pattern mining algorithm for multiple time series data with underlying network structures. Our method has the following properties: (a) Interpretable: it provides interpretable network structures for the data; (b) Automatic: it determines the optimal cut points and the number of clusters automatically; (c) Accurate: it provides reliable clustering performance thanks to the automated algorithm. We evaluate our NGL algorithm on synthetic datasets, outperforming state-of-the-art baselines in terms of accuracy. And extensive experiments on real datasets demonstrate that NGL does indeed obtain interpretable network structure clusters.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"13","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌データベース（TOD）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2023-01-13","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"16"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:23:20.048375+00:00","updated":"2025-01-19T13:23:42.489588+00:00","id":223471}