{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00145125","sets":["1164:2735:7896:8334"]},"path":["8334"],"owner":"11","recid":"145125","title":["Periodic Pattern Mining with Periodical Co-occurrences of Symbols"],"pubdate":{"attribute_name":"公開日","attribute_value":"2015-09-22"},"_buckets":{"deposit":"3314be07-f4a0-49bb-a60e-3027e4ea1cd4"},"_deposit":{"id":"145125","pid":{"type":"depid","value":"145125","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"Periodic Pattern Mining with Periodical Co-occurrences of Symbols","author_link":["222275","222274","222273","222272"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Periodic Pattern Mining with Periodical Co-occurrences of Symbols"},{"subitem_title":"Periodic Pattern Mining with Periodical Co-occurrences of Symbols","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2015-09-22","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Kyoto University／Japan Society for the Promotion of Science"},{"subitem_text_value":"Kyoto University"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Kyoto University / Japan Society for the Promotion of Science","subitem_text_language":"en"},{"subitem_text_value":"Kyoto University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/145125/files/IPSJ-MPS15105007.pdf","label":"IPSJ-MPS15105007.pdf"},"date":[{"dateType":"Available","dateValue":"2017-09-22"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS15105007.pdf","filesize":[{"value":"1.6 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":"ec5e1722-830a-454d-9e5b-d1262a1ede3b","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2015 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Keisuke, Otaki"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Akihiro, Yamamoto"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Keisuke, Otaki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Akihiro, Yamamoto","creatorNameLang":"en"}],"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":"Finding periodic regularity in sequential databases is an important topic in Knowledge Discovery and in pattern mining such regularity is modeled as periodic patterns. Although efficient enumeration algorithms have been studied, applying them to real databases is still challenging because they are noisy and most transactions are not extremely frequent in practice. They cause a combinatorial explosion of patterns and the difficulty of tuning a threshold parameter. To overcome these issues we provide a novel pre-processing method called skeletonization, which was recently introduced for finding sequential patterns. It tries to find clusters of symbols in patterns, aiming at shrinking the space of all possible patterns in order to avoid the combinatorial explosion by considering co-occurrences of symbols. Although the original method cannot allow for periods, we generalize it by using the periodicity. We give experimental results using both synthetic and real datasets to show the effectiveness of our approach, and compare results of mining with and without the skeletonization to see that our method is helpful for mining comprehensive patterns.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Finding periodic regularity in sequential databases is an important topic in Knowledge Discovery and in pattern mining such regularity is modeled as periodic patterns. Although efficient enumeration algorithms have been studied, applying them to real databases is still challenging because they are noisy and most transactions are not extremely frequent in practice. They cause a combinatorial explosion of patterns and the difficulty of tuning a threshold parameter. To overcome these issues we provide a novel pre-processing method called skeletonization, which was recently introduced for finding sequential patterns. It tries to find clusters of symbols in patterns, aiming at shrinking the space of all possible patterns in order to avoid the combinatorial explosion by considering co-occurrences of symbols. Although the original method cannot allow for periods, we generalize it by using the periodicity. We give experimental results using both synthetic and real datasets to show the effectiveness of our approach, and compare results of mining with and without the skeletonization to see that our method is helpful for mining comprehensive patterns.","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":"2015-09-22","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"7","bibliographicVolumeNumber":"2015-MPS-105"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"updated":"2025-01-20T18:28:27.712730+00:00","created":"2025-01-19T00:20:48.175691+00:00","links":{},"id":145125}