{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00241483","sets":["1164:2735:11468:11810"]},"path":["11810"],"owner":"44499","recid":"241483","title":["群知能アルゴリズムによる密集パタン抽出手法の提案"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-12-02"},"_buckets":{"deposit":"6dd7ebf0-0cb1-4b95-9ae3-9015bc7ed3a1"},"_deposit":{"id":"241483","pid":{"type":"depid","value":"241483","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"群知能アルゴリズムによる密集パタン抽出手法の提案","author_link":["664879","664878","664877","664876"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"群知能アルゴリズムによる密集パタン抽出手法の提案"},{"subitem_title":"The Development of Swarm Intelligence-based data mining method to extract dense itemsets","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2024-12-02","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"慶應義塾大学大学院理工学研究科"},{"subitem_text_value":"慶應義塾大学大学院理工学研究科"},{"subitem_text_value":"関西大学教育推進部"},{"subitem_text_value":"慶應義塾大学理工学部"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Science and Technology, Keio University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Science and Technology, Keio University","subitem_text_language":"en"},{"subitem_text_value":"Division for Promotion of Educational Development, Kansai University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Science and Technology, Keio 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/241483/files/IPSJ-MPS24151003.pdf","label":"IPSJ-MPS24151003.pdf"},"date":[{"dateType":"Available","dateValue":"2026-12-02"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS24151003.pdf","filesize":[{"value":"1.3 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":"344df083-860b-4122-827d-02d82a8f575c","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"高橋, 泰平"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"近藤, 雄也"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"須賀, 聖"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"栗原, 聡"}],"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":"密集パタン抽出のための新たなマイニング手法を提案する．トランザクションデータの中には，特定の期間に出現頻度が高まるパタンが存在し，その背後には重要なイベントや要因が潜んでいる可能性がある．従来の手法はデータ全体における出現率の評価を重視しており，局所的な変動を捉えることが困難である．本研究では，スライド窓機構を利用した厳密な手法である「Apriori-window」と，群知能アルゴリズムに基づく近似手法である「plantモデル」を提案する．実データを用いた実験において既存手法と比較して高い精度を示し，また長大なデータに対する時間効率が優れていることを確認した．","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":"2024-12-02","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"2024-MPS-151"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T07:38:57.270935+00:00","created":"2025-01-19T01:46:06.775899+00:00","id":241483}