Mining spatiotemporal association rule based on prevalent sequential patterns 

Mining spatiotemporal association rule based on prevalent sequential patterns | 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 Mining spatiotemporal association rule based on prevalent sequential patterns Ling Wang, Peng Shen, Le Yang, Lingpeng Gui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4687343/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Spatiotemporal association rules mining algorithms can effectively mine the association relationships between spatiotemporal event patterns, which plays an important role in pollution control, weather forecasting and other fields.However,most existing association rule mining algorithms require quantitative time intervals between predefined patterns, and these algorithms do not consider sequential patterns between different spaces. To address these issues, we propose an efficient algorithm for mining spatiotemporal association rule based on prevalent sequential patterns (STARPSP). Firstly, a cross spatiotemporal sequence dataset is constructed by discretizing sequence data from different spaces to mine both sequential patterns in the same space and between different spaces. Secondly, the PrefixSpan algorithm is extended to mine spatiotemporal prevalent sequential patterns. Thirdly, spatiotemporal association rules are automatically obtained based prevalent sequential patterns with time intervals. Finally, STARPSP is compared with other spatiotemporal sequential patterns and association rules mining algorithms on three real datasets. Our experiments show that STARPSP outperforms the other algorithms with respect to both execution time and the number of generated rules. Furthermore, the rules generated by STARPSP contain more spatiotemporal information, which is critical in decision-making. PrefixSpan Prevalent sequential patterns Spatiotemporal association rule Time interval Multiple space Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4687343","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":332374819,"identity":"76da523b-2127-4586-bd5b-677b0d9db61f","order_by":0,"name":"Ling Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACPmYogx+IQWzGBkJa2GBaJBsYGJuJ0wJjGBwgWgs77+HXPDV37DbfSH/+uIDBRnbDAeZnD/A7jC/NmufYs+RtN3IMm2cwpBlvOMBmboBfC4+ZcQ7b4WSzGzmMzTwMhxM3HOBhkyCs5d/hZOMZ6Q+BWv4TpcX4cW7bYTsDiQRDoJYDxNnC/LfvcILEmTeGs2cYJBvPPMxmhlcLP/8Z448zvh22529Pf/C5oMJOtu948zO8WkAWgRQkNoDZoKBixqcYApg/AAl7wupGwSgYBaNgxAIAOVhFWWyst7IAAAAASUVORK5CYII=","orcid":"","institution":"University of Science and Technology Beijing","correspondingAuthor":true,"prefix":"","firstName":"Ling","middleName":"","lastName":"Wang","suffix":""},{"id":332374820,"identity":"36da538f-dcf8-4122-8357-380b610e87a1","order_by":1,"name":"Peng Shen","email":"","orcid":"","institution":"University of Science and Technology Beijing","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Shen","suffix":""},{"id":332374821,"identity":"2ec30424-3d10-4b89-8ea3-c159ae7c2a23","order_by":2,"name":"Le Yang","email":"","orcid":"","institution":"University of Science and Technology Beijing","correspondingAuthor":false,"prefix":"","firstName":"Le","middleName":"","lastName":"Yang","suffix":""},{"id":332374822,"identity":"b24faca4-e644-4b78-8da6-f4f11062a925","order_by":3,"name":"Lingpeng Gui","email":"","orcid":"","institution":"University of Science and Technology Beijing","correspondingAuthor":false,"prefix":"","firstName":"Lingpeng","middleName":"","lastName":"Gui","suffix":""}],"badges":[],"createdAt":"2024-07-04 14:44:14","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4687343/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4687343/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79005839,"identity":"371ab2da-69cc-464b-ba43-c045fe8e520d","added_by":"auto","created_at":"2025-03-22 08:01:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":716179,"visible":true,"origin":"","legend":"","description":"","filename":"KnowledgeandInformationSystems2024.7.4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4687343/v1_covered_c22f2273-b5d8-4abf-a463-cae6308da661.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mining spatiotemporal association rule based on prevalent sequential patterns ","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PrefixSpan, Prevalent sequential patterns, Spatiotemporal association rule, Time interval, Multiple space","lastPublishedDoi":"10.21203/rs.3.rs-4687343/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4687343/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Spatiotemporal association rules mining algorithms can effectively mine the association relationships between spatiotemporal event patterns, which plays an important role in pollution control, weather forecasting and other fields.However,most existing association rule mining algorithms require quantitative time intervals between predefined patterns, and these algorithms do not consider sequential patterns between different spaces. 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