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Insights from such pattern analysis provide numerous benefits, including cost cutting, improved competitive advantage, and increased revenue. However, HUIM methods may discover misleading patterns as they do not evaluate the correlation of extracted patterns. As a consequence, a number of algorithms have been proposed to mine correlated HUIs. These algorithms still suffer from the issue of the computational cost in terms of both time and memory consumption. This paper presents an algorithm, named Efficient Correlated High Utility Pattern Mining (ECoHUPM), to efficiently mine the high utility patterns having strong correlation items. A new data structure based on utility tree (UTtree) named CoUTlist is proposed to store sufficient information for mining the desired patterns. Three pruning properties are introduced to reduce the search space and improve the mining performance. Experiments on sparse, very sparse, dense, and very dense datasets indicate that the proposed ECoHUPM algorithm is efficient as compared to the state\u2010of\u2010the\u2010art CoHUIM and CoHUI\u2010Miner algorithms in terms of both time and memory consumption.<\/jats:p>","DOI":"10.1155\/2021\/7310137","type":"journal-article","created":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T22:08:08Z","timestamp":1627510088000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Efficient Utility Tree\u2010Based Algorithm to Mine High Utility Patterns Having Strong Correlation"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9571-4329","authenticated-orcid":false,"given":"Rashad","family":"Saeed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3019-4136","authenticated-orcid":false,"given":"Azhar","family":"Rauf","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7872-1877","authenticated-orcid":false,"given":"Fahmi H.","family":"Quradaa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2602-9533","authenticated-orcid":false,"given":"Syed Muhammad","family":"Asim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,7,28]]},"reference":[{"key":"e_1_2_9_1_2","volume-title":"How Much Data is Generated Each Day?","author":"Desjardins J.","year":"2019"},{"key":"e_1_2_9_2_2","volume-title":"Data Mining: Concepts Techniques","author":"Han J.","year":"2011"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1207"},{"key":"e_1_2_9_4_2","first-page":"54","article-title":"A survey of sequential pattern mining","volume":"1","author":"Fournier-Viger P.","year":"2017","journal-title":"Data Science and Pattern Recognition"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-018-9629-z"},{"key":"e_1_2_9_6_2","first-page":"131","volume-title":"Studies in Big Data","author":"Qu J.-F.","year":"2019"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-07821-2_2"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-07821-2_18"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbt074"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-016-0986-0"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2009.46"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2016.04.016"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2974104"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3363571"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.08.028"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/11430919_79"},{"key":"e_1_2_9_17_2","doi-asserted-by":"crossref","unstructured":"DawarS.andGoyalV. 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