{"updated":"2025-01-22T23:22:15.396053+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00017374","sets":["934:1022:1023:1024"]},"path":["1024"],"owner":"1","recid":"17374","title":["拡張出現マッチングを用いた制約付きノイズ許容極小順序木パターンの発見"],"pubdate":{"attribute_name":"公開日","attribute_value":"2008-12-26"},"_buckets":{"deposit":"f9747dff-2c5c-4c71-9c37-df31b8ded63a"},"_deposit":{"id":"17374","pid":{"type":"depid","value":"17374","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"拡張出現マッチングを用いた制約付きノイズ許容極小順序木パターンの発見","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"拡張出現マッチングを用いた制約付きノイズ許容極小順序木パターンの発見"},{"subitem_title":"Mining Noise-tolerant Minimal Constrained Ordered Subtrees by Using Extended Occurrence Matching","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"研究論文","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2008-12-26","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"神戸大学自然科学系先端融合研究環"},{"subitem_text_value":"神戸大学大学院工学研究科"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Organization of Advanced Science and Technology, Kobe University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Kobe 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/17374/files/IPSJ-TOD0103004.pdf"},"date":[{"dateType":"Available","dateValue":"2010-12-26"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TOD0103004.pdf","filesize":[{"value":"428.5 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"13"},{"tax":["include_tax"],"price":"0","billingrole":"39"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"2a1843e2-e453-4122-94bf-e9803fd972b7","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2008 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"尾崎, 知伸"},{"creatorName":"大川剛直"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tomonobu, Ozaki","creatorNameLang":"en"},{"creatorName":"Takenao, Ohkawa","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":"近年，大量のパターンが抽出されるという構造データを対象とした頻出パターン発見の問題に対し，(1) 頻出パターンの代表元のみを発見する手法や，(2) 利用者により与えられる制約を満たすパターンのみを発見する手法などが提案されている．本論文では，順序木データベースを対象とした両者の統合アプローチとして，制約下でのノイズを許容した極小元である，(1) 制約付きδ-フリー順序木パターンと，(2) 制約付き Δ-トレランス順序木パターンの発見問題について議論する．この問題を解決するために，本論文では，拡張出現マッチングと，それに基づく枝刈り手法をともなう3種のアルゴリズムを提案する．合成データと実データを用いた比較実験により，抽出されるパターン数や実行時間の観点から，提案手法の有効性が確認された．","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Frequent pattern miners for structured data often discover huge number of patterns. To alleviate this problem, two major approaches, (1) condensed representation mining and (2) constraint-based mining, have been proposed. In this paper, as a technique for integrating these two approaches in ordered subtree mining, we focus on mining ‘noise-tolerant minimal patterns under constraints’ and discuss the problems of mining (1) δ-free constrained ordered subtrees and (2) Δ-tolerance constrained ordered subtrees. To achieve this objective, we propose three kinds of algorithms having pruning capability based on extended occurrence-matching. The results of experiments with synthetic and real world datasets show that, compared with a naive algorithm, the proposed algorithms succeed in reducing the number of extracted patterns and execution time.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"35","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌データベース（TOD）"}],"bibliographicPageStart":"20","bibliographicIssueDates":{"bibliographicIssueDate":"2008-12-26","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"1"}]},"relation_version_is_last":true,"weko_creator_id":"1"},"created":"2025-01-18T22:50:23.566518+00:00","id":17374,"links":{}}