{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T23:21:16Z","timestamp":1783984876033,"version":"3.55.0"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Sciences"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1016\/j.ins.2024.121218","type":"journal-article","created":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T18:40:01Z","timestamp":1721846401000},"page":"121218","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":24,"special_numbering":"C","title":["Efficient high utility itemset mining without the join operation"],"prefix":"10.1016","volume":"681","author":[{"given":"Yihe","family":"Yan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinzheng","family":"Niu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philippe","family":"Fournier-Viger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Libin","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-3290-1036","authenticated-orcid":false,"given":"Fan","family":"Min","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.ins.2024.121218_br0010","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1145\/170036.170072","article-title":"Mining association rules between sets of items in large databases","volume":"22","author":"Agrawal","year":"1993","journal-title":"SIGMOD Rec."},{"key":"10.1016\/j.ins.2024.121218_br0020","series-title":"High-Utility Pattern Mining: Theory, Algorithms and Applications","first-page":"1","article-title":"A survey of high utility itemset mining","author":"Fournier-Viger","year":"2019"},{"key":"10.1016\/j.ins.2024.121218_br0030","series-title":"Proceedings of the 2004 SIAM International Conference on Data Mining","first-page":"482","article-title":"A foundational approach to mining itemset utilities from databases","author":"Yao","year":"2004"},{"key":"10.1016\/j.ins.2024.121218_br0040","series-title":"Proceedings of the 1994 International Conference on Very Large Data Bases (VLDB'94)","first-page":"487","article-title":"Fast algorithms for mining association rules","volume":"vol. 1215","author":"Agrawal","year":"1994"},{"issue":"2","key":"10.1016\/j.ins.2024.121218_br0050","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/335191.335372","article-title":"Mining frequent patterns without candidate generation","volume":"29","author":"Han","year":"2000","journal-title":"SIGMOD Rec."},{"issue":"3","key":"10.1016\/j.ins.2024.121218_br0060","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1109\/69.846291","article-title":"Scalable algorithms for association mining","volume":"12","author":"Zaki","year":"2000","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"10.1016\/j.ins.2024.121218_br0070","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1145\/568271.223813","article-title":"An effective hash-based algorithm for mining association rules","volume":"24","author":"Park","year":"1995","journal-title":"SIGMOD Rec."},{"key":"10.1016\/j.ins.2024.121218_br0080","series-title":"Proceedings of the 21th International Conference on Very Large Data Bases, VLDB '95","first-page":"432","article-title":"An efficient algorithm for mining association rules in large databases","author":"Savasere","year":"1995"},{"key":"10.1016\/j.ins.2024.121218_br0090","series-title":"Vldb","first-page":"134","article-title":"Sampling large databases for association rules","volume":"vol. 96","author":"Toivonen","year":"1996"},{"issue":"3","key":"10.1016\/j.ins.2024.121218_br0100","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1006\/jpdc.2000.1693","article-title":"A tree projection algorithm for generation of frequent item sets","volume":"61","author":"Agarwal","year":"2001","journal-title":"J. Parallel Distrib. Comput."},{"key":"10.1016\/j.ins.2024.121218_br0110","series-title":"Advances in Knowledge Discovery and Data Mining","first-page":"689","article-title":"A two-phase algorithm for fast discovery of high utility itemsets","author":"Liu","year":"2005"},{"issue":"8","key":"10.1016\/j.ins.2024.121218_br0120","doi-asserted-by":"crossref","first-page":"1772","DOI":"10.1109\/TKDE.2012.59","article-title":"Efficient algorithms for mining high utility itemsets from transactional databases","volume":"25","author":"Tseng","year":"2013","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"12","key":"10.1016\/j.ins.2024.121218_br0130","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.1109\/TKDE.2009.46","article-title":"Efficient tree structures for high utility pattern mining in incremental databases","volume":"21","author":"Ahmed","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"8","key":"10.1016\/j.ins.2024.121218_br0140","doi-asserted-by":"crossref","first-page":"3861","DOI":"10.1016\/j.eswa.2013.11.038","article-title":"High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates","volume":"41","author":"Yun","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.ins.2024.121218_br0150","series-title":"Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM '12","first-page":"55","article-title":"Mining high utility itemsets without candidate generation","author":"Liu","year":"2012"},{"key":"10.1016\/j.ins.2024.121218_br0160","series-title":"Proceedings of the 2012 IEEE 12th International Conference on Data Mining, ICDM '12","first-page":"984","article-title":"Direct discovery of high utility itemsets without candidate generation","author":"Liu","year":"2012"},{"key":"10.1016\/j.ins.2024.121218_br0170","series-title":"Advances in Artificial Intelligence and Soft Computing","first-page":"530","article-title":"Efim: a highly efficient algorithm for high-utility itemset mining","author":"Zida","year":"2015"},{"key":"10.1016\/j.ins.2024.121218_br0180","series-title":"Foundations of Intelligent Systems","first-page":"83","article-title":"Fhm: faster high-utility itemset mining using estimated utility co-occurrence pruning","author":"Fournier-Viger","year":"2014"},{"key":"10.1016\/j.ins.2024.121218_br0190","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.eswa.2017.08.028","article-title":"Hminer: efficiently mining high utility itemsets","volume":"90","author":"Krishnamoorthy","year":"2017","journal-title":"Expert Syst. Appl."},{"issue":"5","key":"10.1016\/j.ins.2024.121218_br0200","doi-asserted-by":"crossref","first-page":"2371","DOI":"10.1016\/j.eswa.2014.11.001","article-title":"Pruning strategies for mining high utility itemsets","volume":"42","author":"Krishnamoorthy","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.ins.2024.121218_br0210","series-title":"High-Utility Pattern Mining: Theory, Algorithms and Applications","first-page":"131","article-title":"Efficient algorithms for high utility itemset mining without candidate generation","author":"Qu","year":"2019"},{"key":"10.1016\/j.ins.2024.121218_br0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.108865","article-title":"Ubp-miner: an efficient bit based high utility itemset mining algorithm","volume":"248","author":"Wu","year":"2022","journal-title":"Knowl.-Based Syst."},{"issue":"2","key":"10.1016\/j.ins.2024.121218_br0230","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1007\/s10115-016-0989-x","article-title":"Indexed list-based high utility pattern mining with utility upper-bound reduction and pattern combination techniques","volume":"51","author":"Ryang","year":"2017","journal-title":"Knowl. Inf. Syst."},{"key":"10.1016\/j.ins.2024.121218_br0240","series-title":"Advances in Knowledge Discovery and Data Mining","first-page":"196","article-title":"mhuiminer: a fast high utility itemset mining algorithm for sparse datasets","author":"Peng","year":"2017"},{"key":"10.1016\/j.ins.2024.121218_br0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106457","article-title":"Mining high utility itemsets using extended chain structure and utility machine","volume":"208","author":"Qu","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.ins.2024.121218_br0260","first-page":"1","article-title":"Mining high utility itemsets using prefix trees and utility vectors","author":"Qu","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"7","key":"10.1016\/j.ins.2024.121218_br0270","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1007\/s10489-017-1057-2","article-title":"Efficient high utility itemset mining using buffered utility-lists","volume":"48","author":"Duong","year":"2018","journal-title":"Appl. Intell."},{"key":"10.1016\/j.ins.2024.121218_br0280","series-title":"19th Annual Symposium on Foundations of Computer Science (sfcs 1978)","first-page":"8","article-title":"A dichromatic framework for balanced trees","author":"Guibas","year":"1978"},{"issue":"1","key":"10.1016\/j.ins.2024.121218_br0290","doi-asserted-by":"crossref","DOI":"10.1145\/3462636","article-title":"Mining high utility itemsets with hill climbing and simulated annealing","volume":"13","author":"Nawaz","year":"2021","journal-title":"ACM Trans. Manage. Inf. Syst."},{"issue":"17","key":"10.1016\/j.ins.2024.121218_br0300","doi-asserted-by":"crossref","first-page":"5103","DOI":"10.1007\/s00500-016-2106-1","article-title":"A binary pso approach to mine high-utility itemsets","volume":"21","author":"Lin","year":"2017","journal-title":"Soft Comput."},{"key":"10.1016\/j.ins.2024.121218_br0310","series-title":"Advances in Swarm Intelligence: 12th International Conference, ICSI 2021","first-page":"407","article-title":"Artificial fish swarm algorithm for mining high utility itemsets","author":"Song","year":"2021"},{"issue":"6","key":"10.1016\/j.ins.2024.121218_br0320","doi-asserted-by":"crossref","first-page":"7419","DOI":"10.1016\/j.eswa.2010.12.082","article-title":"An effective tree structure for mining high utility itemsets","volume":"38","author":"Lin","year":"2011","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"10.1016\/j.ins.2024.121218_br0330","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s11704-016-6245-4","article-title":"Cls-miner: efficient and effective closed high-utility itemset mining","volume":"13","author":"Dam","year":"2019","journal-title":"Front. Comput. Sci."},{"issue":"8","key":"10.1016\/j.ins.2024.121218_br0340","doi-asserted-by":"crossref","first-page":"8839","DOI":"10.1007\/s10489-021-02922-1","article-title":"Discovery of closed high utility itemsets using a fast nature-inspired ant colony algorithm","volume":"52","author":"Pramanik","year":"2022","journal-title":"Appl. Intell."},{"key":"10.1016\/j.ins.2024.121218_br0350","doi-asserted-by":"crossref","DOI":"10.1016\/j.datak.2021.101927","article-title":"Mining closed high utility itemsets based on propositional satisfiability","volume":"136","author":"Hidouri","year":"2021","journal-title":"Data Knowl. Eng."},{"key":"10.1016\/j.ins.2024.121218_br0360","series-title":"2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","first-page":"187","article-title":"Mining closed+ high utility itemsets without candidate generation","author":"Wu","year":"2015"},{"key":"10.1016\/j.ins.2024.121218_br0370","series-title":"2021 IEEE International Conference on Big Data (Big Data)","first-page":"622","article-title":"On minimal and maximal high utility itemsets mining using propositional satisfiability","author":"Hidouri","year":"2021"},{"key":"10.1016\/j.ins.2024.121218_br0380","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109921","article-title":"Efficient algorithms for mining closed and maximal high utility itemsets","volume":"257","author":"Duong","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.ins.2024.121218_br0390","series-title":"Advanced Data Mining and Applications","first-page":"30","article-title":"Novel concise representations of high utility itemsets using generator patterns","author":"Fournier-Viger","year":"2014"},{"issue":"13","key":"10.1016\/j.ins.2024.121218_br0400","doi-asserted-by":"crossref","first-page":"5754","DOI":"10.1016\/j.eswa.2015.02.051","article-title":"An efficient approach for mining association rules from high utility itemsets","volume":"42","author":"Sahoo","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.ins.2024.121218_br0410","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.ins.2020.08.028","article-title":"Efficient top-k high utility itemset mining on massive data","volume":"557","author":"Han","year":"2021","journal-title":"Inf. Sci."},{"issue":"3","key":"10.1016\/j.ins.2024.121218_br0420","doi-asserted-by":"crossref","first-page":"1078","DOI":"10.1007\/s10489-018-1316-x","article-title":"Tkeh: an efficient algorithm for mining top-k high utility itemsets","volume":"49","author":"Singh","year":"2019","journal-title":"Appl. Intell."},{"key":"10.1016\/j.ins.2024.121218_br0430","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.eswa.2018.09.051","article-title":"Mining top-k high utility itemsets with effective threshold raising strategies","volume":"117","author":"Krishnamoorthy","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.ins.2024.121218_br0440","series-title":"Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2016","first-page":"36","article-title":"The spmf open-source data mining library version 2","author":"Fournier-Viger","year":"2016"},{"issue":"2","key":"10.1016\/j.ins.2024.121218_br0450","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1007\/s10489-019-01531-3","article-title":"Extracting relations of crime rates through fuzzy association rules mining","volume":"50","author":"Zhang","year":"2020","journal-title":"Appl. Intell."},{"key":"10.1016\/j.ins.2024.121218_br0460","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.115122","article-title":"High utility itemset mining using binary differential evolution: an application to customer segmentation","volume":"181","author":"Krishna","year":"2021","journal-title":"Expert Syst. Appl."}],"container-title":["Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0020025524011320?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0020025524011320?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T17:23:40Z","timestamp":1728494620000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S0020025524011320"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":46,"alternative-id":["S0020025524011320"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.ins.2024.121218","relation":{},"ISSN":["0020-0255"],"issn-type":[{"value":"0020-0255","type":"print"}],"subject":[],"published":{"date-parts":[[2024,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Efficient high utility itemset mining without the join operation","name":"articletitle","label":"Article Title"},{"value":"Information Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.ins.2024.121218","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"121218"}}