{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T06:58:14Z","timestamp":1758265094856,"version":"3.41.2"},"reference-count":28,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2016,8,2]],"date-time":"2016-08-02T00:00:00Z","timestamp":1470096000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61402396","61402203","61379066","61070047"],"award-info":[{"award-number":["61402396","61402203","61379066","61070047"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2017,3,25]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Vertical data structure is very important for closed frequent itemset mining. All closed frequent itemsets can be found by simply using the operations of AND\/OR. However, it consumes a large amount of storage space, especially in the case of large\u2010size dataset. This paper proposes an algorithm for mining closed frequent itemsets based on a new vertical data structure. The proposed data structure is helpful to save storage space by using a multi\u2010layer index. At the same time, numerous CPU and graphics processing unit can be employed in parallel to achieve high\u2010efficiency computing. Especially when dealing with large datasets, the proposed algorithm can obtain a high\u2010speed computing with the help of graphics processing unit. The improved vertical structure reduces the storage space of the data. The experimental results show that our proposed algorithm requires much less computation time than other related methods. Copyright \u00a9 2016 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/cpe.3904","type":"journal-article","created":{"date-parts":[[2016,8,2]],"date-time":"2016-08-02T22:56:52Z","timestamp":1470178612000},"source":"Crossref","is-referenced-by-count":11,"title":["A new closed frequent itemset mining algorithm based on GPU and improved vertical structure"],"prefix":"10.1002","volume":"29","author":[{"given":"Yun","family":"Li","sequence":"first","affiliation":[{"name":"College of Information Engineering Yangzhou University Yangzhou 225009 China"}]},{"given":"Jie","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Information Engineering Yangzhou University Yangzhou 225009 China"}]},{"given":"Yun\u2010Hao","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of Information Engineering Yangzhou University Yangzhou 225009 China"}]},{"given":"Ling","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information Engineering Yangzhou University Yangzhou 225009 China"}]}],"member":"311","published-online":{"date-parts":[[2016,8,2]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_8_2_1","DOI":"10.1016\/j.future.2013.10.004"},{"doi-asserted-by":"crossref","unstructured":"AgrawalR Imieli\u0144skiT SwamiA..Mining association rules between sets of items in large databases. InIn Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data:New York USA 1993;207\u2013216.","key":"e_1_2_8_3_1","DOI":"10.1145\/170035.170072"},{"doi-asserted-by":"publisher","key":"e_1_2_8_4_1","DOI":"10.1016\/j.jcss.2009.05.002"},{"doi-asserted-by":"crossref","unstructured":"KrajcaP OutrataJ VychodilV..Using frequent closed itemsets for data dimensionality reduction. InIn Proceedings of the 11th IEEE International Conference on Data Mining (ICDM):Washington DC USA 2011;1128\u20131133.","key":"e_1_2_8_5_1","DOI":"10.1109\/ICDM.2011.154"},{"doi-asserted-by":"crossref","unstructured":"ChenX HeY ChenP MiaoS SongW YueM..HPFP\u2010Miner: a novel parallel frequent itemset mining algorithm. InIn Proceedings of the 5th International Conference on Natural Computation:Washington DC USA 2009;139\u2013143.","key":"e_1_2_8_6_1","DOI":"10.1109\/ICNC.2009.263"},{"key":"e_1_2_8_7_1","first-page":"12","article-title":"A parallel frequent item sets mining based on vertical FP tree","volume":"40","author":"Xu J","year":"2012","journal-title":"Computer and Digital Engineer"},{"doi-asserted-by":"crossref","unstructured":"\u00d6zdoganG\u00d6 AbulO.Task\u2010parallel FP\u2010growth on cluster computers. InIn Proceedings of the 25th International Symposium on Computer and Information Sciences:Dordrecht Netherlands 2010;383\u2013388.","key":"e_1_2_8_8_1","DOI":"10.1007\/978-90-481-9794-1_71"},{"unstructured":"HuJ Yang\u2010LiX.A fast parallel association rules mining algorithm based on FP\u2010forest. InIn Proceedings of the 5th International Symposium on Neural Networks Part II:Berlin Germany 2008;24\u201328.","key":"e_1_2_8_9_1"},{"doi-asserted-by":"crossref","unstructured":"LiQ ChangS.Generating closed frequent itemsets with the frequent pattern list. InIn Proceedings of the 2nd International Workshop on Database Technology and Applications (DBTA):Washington DC USA 2010;1\u20134.","key":"e_1_2_8_10_1","DOI":"10.1109\/DBTA.2010.5658741"},{"unstructured":"WenL.An efficient algorithm for mining frequent closed itemset. InIn Proceedings of the 5th World Congress on Intelligent Control and Automation:Washington DC USA 2004;4296\u20134299.","key":"e_1_2_8_11_1"},{"doi-asserted-by":"crossref","unstructured":"ZhuF QuQ LoD YanX HanJ YuPS.Mining top\u2010k large structural patterns in a massive network. InIn Proceedings of the International Conference on Very Large Data Bases (VLDB) vol.\u00a04:Saratoga CA USA 2011;807\u2013818.","key":"e_1_2_8_12_1","DOI":"10.14778\/3402707.3402720"},{"doi-asserted-by":"crossref","unstructured":"YanX ZhuF HanJ.gApprox: mining frequent approximate patterns from a massive network. InIn Proceedings of 2007 International Conference on Data Mining (ICDM):Washington DC USA 2007;445\u2013450.","key":"e_1_2_8_13_1","DOI":"10.1109\/ICDM.2007.36"},{"doi-asserted-by":"crossref","unstructured":"ZhuF YanX HanJ YuPS.Gprune: A constraint pushing framework for graph pattern mining. InIn Proceedings of the Pacific\u2010Asia Conference on Knowledge Discovery and Data Mining (PAKDD):Berlin Germany 2007;388\u2013400.","key":"e_1_2_8_14_1","DOI":"10.1007\/978-3-540-71701-0_38"},{"doi-asserted-by":"crossref","unstructured":"ZhuF YanX HanJ YuPS ChengH..Mining colossal frequent patterns by core pattern fusion. InIn Proceedings of the 29th IEEE International Conference on Data Engineering (ICDE):Washington DC USA 2007;706\u2013715.","key":"e_1_2_8_15_1","DOI":"10.1109\/ICDE.2007.367916"},{"doi-asserted-by":"publisher","key":"e_1_2_8_16_1","DOI":"10.1016\/j.ins.2013.12.062"},{"doi-asserted-by":"publisher","key":"e_1_2_8_17_1","DOI":"10.1093\/comjnl\/bxt113"},{"doi-asserted-by":"publisher","key":"e_1_2_8_18_1","DOI":"10.1016\/j.neucom.2013.05.046"},{"doi-asserted-by":"publisher","key":"e_1_2_8_19_1","DOI":"10.1016\/j.eswa.2013.07.090"},{"doi-asserted-by":"publisher","key":"e_1_2_8_20_1","DOI":"10.1166\/jctn.2014.3405"},{"doi-asserted-by":"publisher","key":"e_1_2_8_21_1","DOI":"10.1016\/j.future.2013.07.001"},{"doi-asserted-by":"publisher","key":"e_1_2_8_22_1","DOI":"10.1002\/cpe.3018"},{"doi-asserted-by":"publisher","key":"e_1_2_8_23_1","DOI":"10.1002\/cpe.2845"},{"issue":"14","key":"e_1_2_8_24_1","first-page":"59","article-title":"A closed frequent item sets mining algorithm based on GPU","volume":"37","author":"Li HF","year":"2011","journal-title":"Computer Engineering"},{"doi-asserted-by":"crossref","unstructured":"TeodoroG MarianoN FerreiraR WMJr.Tree projection\u2010based frequent itemset mining on multicore CPUs and GPUs. InIn Proceedings of the 22nd International Symposium on Computer Architecture and High Performance Computing:Washington DC USA 2010;47\u201354.","key":"e_1_2_8_25_1","DOI":"10.1109\/SBAC-PAD.2010.15"},{"doi-asserted-by":"crossref","unstructured":"LiY XuJ ZhangX LiC ZhangY.An incremental closed frequent itemsets mining algorithm based on shadow prefix tree. InIn Proceedings of the 10th Web Information System and Application Conference (WISA):Washington DC USA 2013;440\u2013445.","key":"e_1_2_8_26_1","DOI":"10.1109\/WISA.2013.89"},{"doi-asserted-by":"publisher","key":"e_1_2_8_27_1","DOI":"10.1016\/j.compfluid.2013.05.021"},{"doi-asserted-by":"publisher","key":"e_1_2_8_28_1","DOI":"10.1016\/j.jpdc.2013.07.021"},{"unstructured":"IBM Quest Synthetic Data Generator 2015. (Available from:http:\/\/www.philippe-fournier-viger.com\/spmf\/datasets\/IBM_Quest_data_generator.zip) [Accessed on March 2015].","key":"e_1_2_8_29_1"}],"container-title":["Concurrency and Computation: Practice and Experience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fcpe.3904","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fcpe.3904","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cpe.3904","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T11:40:08Z","timestamp":1749037208000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/cpe.3904"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,8,2]]},"references-count":28,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2017,3,25]]}},"alternative-id":["10.1002\/cpe.3904"],"URL":"https:\/\/doi.org\/10.1002\/cpe.3904","archive":["Portico"],"relation":{},"ISSN":["1532-0626","1532-0634"],"issn-type":[{"type":"print","value":"1532-0626"},{"type":"electronic","value":"1532-0634"}],"subject":[],"published":{"date-parts":[[2016,8,2]]},"article-number":"e3904"}}