An Novel Efficient Bi-Objective Evolutionary Algorithm for Frequent and High Utility Itemsets Mining

An Novel Efficient Bi-Objective Evolutionary Algorithm for Frequent and High Utility Itemsets Mining | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Novel Efficient Bi-Objective Evolutionary Algorithm for Frequent and High Utility Itemsets Mining Li Ma, Chongyang Li, Hengyang Lu, Wei Fang, Jerry Chun-Wei Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4450561/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Feb, 2025 Read the published version in Memetic Computing → Version 1 posted 7 You are reading this latest preprint version Abstract Mining frequent and high utility itemsets (FHUIs) from transaction database is an important task in data mining. In order to overcome the difficulties of parameter setting and huge search space in traditional algorithms for mining FHUIs, the task of mining FHUIs was modeled as a bi-objective problem and then solved by multi-objective evolutionary algorithms (MOEAs) in previous works. However, MOEAs may be inefficient when the number of transactions and items in the transaction database becomes large. To address this problem, we propose a novel efficient bi-objective evolutionary algorithm for mining FHUIs (NBOEA-FHUI). In NBOEA-FHUI, a novel initialization strategy is proposed, which takes the support, utility, and diversity of the initial population into account. The proposed initial strategy can make the initial population have relative high utility and support values with high population diversity. To improve the quality of the offspring, a method for estimating the support and utility value of itemsets and an offspring generation strategy are proposed in NBOEA-FHUI. The support and utility values of itemsets which are roughly proportional to their true values can be calculated by the estimation method with little computation. The proposed offspring generation strategy can generate better offspring based on the estimated support and utility value. Experimental results on several real datasets demonstrate that the proposed algorithm has better performance than the state-of-the-art MOEAs in terms of the convergence speed, search efficiency, and solution accuracy in the task of mining FHUIs. Data mining Frequent and high utility itemsets Multi-objective optimization Evolutionary algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Feb, 2025 Read the published version in Memetic Computing → Version 1 posted Editorial decision: Revision requested 07 Jun, 2024 Reviews received at journal 02 Jun, 2024 Reviewers agreed at journal 02 Jun, 2024 Reviewers invited by journal 31 May, 2024 Editor assigned by journal 22 May, 2024 Submission checks completed at journal 22 May, 2024 First submitted to journal 20 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4450561","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309566850,"identity":"26ecf0e2-f9fb-40f6-aa38-5c49398c82ff","order_by":0,"name":"Li Ma","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Ma","suffix":""},{"id":309566851,"identity":"e357d60b-b39c-40ed-9dd3-726fe91fed86","order_by":1,"name":"Chongyang Li","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Chongyang","middleName":"","lastName":"Li","suffix":""},{"id":309566852,"identity":"cbc1aa32-0ba5-4c01-8a7b-4af8a04dd3b3","order_by":2,"name":"Hengyang Lu","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Hengyang","middleName":"","lastName":"Lu","suffix":""},{"id":309566853,"identity":"da64bce8-d597-4cab-9b5f-f2066b327e6b","order_by":3,"name":"Wei Fang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYBAC9gYwJSHH2IwQNMCrhecARIsxyVoYEhuQBAloYT97+DVPhUV6czvzs4dfd9jlMbA3b5NgqLmDWwtPXpo1zxmJ3MZmNnNj2TPJxQw8x8okGI49w6nFniHHzDi3DaSFwUxaso05sUEix0yCseEwblv43wC1/JNIZ2xm/wbUUp/YIP+GgBaJHOPHuQ0SCYzNPGaSH9sOA23hIaTljRnzn2MSho3NPGXSjG3HE9t40ootEo7hc1iO8ccZNXXyhv3Ht0n+bKtO7Gc/vPHGhxrcWoCATQJEGjYwMDDzgLggXgI+DUCFH0CkPBAz/sCvchSMglEwCkYoAABYD03P9K11OgAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangnan University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Fang","suffix":""},{"id":309566854,"identity":"bb7a9e83-8e7e-4603-a970-19bb0292eca0","order_by":4,"name":"Jerry Chun-Wei Lin","email":"","orcid":"","institution":"Silesian University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jerry","middleName":"Chun-Wei","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2024-05-20 17:59:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4450561/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4450561/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12293-025-00437-7","type":"published","date":"2025-02-07T15:57:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75930431,"identity":"601e25cd-d993-49f3-8e86-7abe1d8d6b2c","added_by":"auto","created_at":"2025-02-10 16:11:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":827317,"visible":true,"origin":"","legend":"","description":"","filename":"Titlelabel123.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4450561/v1_covered_d7bd53b9-a433-4bf9-ade5-0834d01af770.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Novel Efficient Bi-Objective Evolutionary Algorithm for Frequent and High Utility Itemsets Mining","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"memetic-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meme","sideBox":"Learn more about [Memetic Computing](http://link.springer.com/journal/12289)","snPcode":"12293","submissionUrl":"https://submission.nature.com/new-submission/12293/3","title":"Memetic Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Data mining, Frequent and high utility itemsets, Multi-objective optimization, Evolutionary algorithm","lastPublishedDoi":"10.21203/rs.3.rs-4450561/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4450561/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMining frequent and high utility itemsets (FHUIs) from transaction database is an important task in data mining. 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