Association analysis using the mining of positive and negative association rules

Association analysis using the mining of positive and negative association rules | 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 Association analysis using the mining of positive and negative association rules Thanh Do Van, Ha Dinh Thi, Phuong Truong Duc This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4138411/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 May, 2025 Read the published version in Knowledge and Information Systems → Version 1 posted 9 You are reading this latest preprint version Abstract Association analysis using the mining of positive and negative association rules (PNARs) has so far been mainly based on mining rules of forms A⇒B, A⇒ℸB, ℸA⇒B, and ℸA⇒ℸB. These are called narrow PNARs (NPNARs). Most existing algorithms for mining NPNARs usually exploit the upward closure property of negative itemsets, while few exploit the downward closure property. NPNARs mined by algorithms built under the first approach are inconsistent with human thinking and unsuitable for explaining association analysis. NPNARs mined by algorithms under the second approach are consistent with human thinking, but generally, they are just positive association rules or negative dependency relationships and are not intuitively described as the NPNARs above. Thus, they are confusing and difficult to interpret. So far, no algorithm built under both approaches has found all valid NPNARs. This work proposes an algorithm based solely on (positive) items in transaction databases under the second approach to mine NPNARs. The algorithm is developed based on equivalence classes and the support-confidence framework. Two phases of the association rule mining process are executed concurrently. The algorithm is sound and complete. Its computational complexity is also estimated. The experiment shows the application prospect of the proposed algorithm in the association analysis of co-occurrence and non-co-occurrence events. Data mining negative association rule association analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 May, 2025 Read the published version in Knowledge and Information Systems → Version 1 posted Editorial decision: Revision requested 09 Aug, 2024 Reviews received at journal 30 Apr, 2024 Reviews received at journal 27 Apr, 2024 Reviewers agreed at journal 31 Mar, 2024 Reviewers agreed at journal 31 Mar, 2024 Reviewers invited by journal 31 Mar, 2024 Editor assigned by journal 28 Mar, 2024 Submission checks completed at journal 21 Mar, 2024 First submitted to journal 20 Mar, 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-4138411","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282226469,"identity":"f4a17e6e-30e9-423b-9814-1a3e962ea0c8","order_by":0,"name":"Thanh Do Van","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYDCCAzBSgvngAx6YKA8O1QgtCSAtbMkGQJUSpGjhMZMgSgvf8R7Dz4U/7sibSzeYVbxtO1zHP7uB8cHbNobE7Ti0SJ45Yyw9I+GZ4c45B9Juzm07LCFx5wCz4Vyglp0N2LUY3EhLkOZJOMy44UbCsdu8QC0MNxLYpHnbGIwNDuDUkvwbqMV+w43EtmKQFvkbCey/8WtJPgayJXHDjWQ2ZpAWA6AtQAaDHC4tkmcOH7PmSTucvOFGGrPknHPpkhtvJDYDGRI4tfAdb2y+zWNz2HbDjfyPH96UWfPL3Ug+CGTY8ODSgg6agZixgQEWP8SAOqJVjoJRMApGwcgBACtsZGFDrq9TAAAAAElFTkSuQmCC","orcid":"","institution":"Duy Tan University","correspondingAuthor":true,"prefix":"","firstName":"Thanh","middleName":"Do","lastName":"Van","suffix":""},{"id":282226470,"identity":"62a8a87b-1b14-4f19-8d92-7d2f59d6e32c","order_by":1,"name":"Ha Dinh Thi","email":"","orcid":"","institution":"Thuongmai University","correspondingAuthor":false,"prefix":"","firstName":"Ha","middleName":"Dinh","lastName":"Thi","suffix":""},{"id":282226471,"identity":"3f1742d9-919d-4b82-8649-6896085b2201","order_by":2,"name":"Phuong Truong Duc","email":"","orcid":"","institution":"Thudo University","correspondingAuthor":false,"prefix":"","firstName":"Phuong","middleName":"Truong","lastName":"Duc","suffix":""}],"badges":[],"createdAt":"2024-03-20 16:03:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4138411/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4138411/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10115-025-02445-y","type":"published","date":"2025-05-08T15:56:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82537404,"identity":"6b2b745c-241b-4c42-8549-1b50c000729e","added_by":"auto","created_at":"2025-05-12 16:02:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":566876,"visible":true,"origin":"","legend":"","description":"","filename":"KAIS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4138411/v1_covered_1c8ebdba-8dd9-490a-991f-f69c59bf48ca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association analysis using the mining of positive and negative association rules","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":"knowledge-and-information-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"kais","sideBox":"Learn more about [Knowledge and Information Systems](http://link.springer.com/journal/10115)","snPcode":"10115","submissionUrl":"https://submission.nature.com/new-submission/10115/3","title":"Knowledge and Information Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Data mining, negative association rule, association analysis","lastPublishedDoi":"10.21203/rs.3.rs-4138411/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4138411/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Association analysis using the mining of positive and negative association rules (PNARs) has so far been mainly based on mining rules of forms A⇒B, A⇒ℸB, ℸA⇒B, and ℸA⇒ℸB. 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