STSNN-DPC: spatio-temporal shared nearest neighbors and density peaks based clustering method

STSNN-DPC: spatio-temporal shared nearest neighbors and density peaks based clustering method | 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 STSNN-DPC: spatio-temporal shared nearest neighbors and density peaks based clustering method Fengling Zhang, Shengqiang Huang, Haiyan Zhang, Yonglong Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5381784/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Density peaks clustering (DPC) algorithm is a novel clustering method, which offers advantages such as simple parameter adjustment and ease of implementation. DPC-based clustering algorithms can effectively analyze and mine data to discover hidden patterns, making it a significant research topic. Existing research primarily focuses on the neighborhood of data points at spatial latitude and clusters these points based on neighborhood density. However, we find that there is a correlation between the spatial and temporal neighborhoods of spatio-temporal data. Ignoring this correlation significantly reduces the accuracy of clustering results for spatio-temporal data. In this paper, we propose a spatio-temporal shared nearest neighbors and density peaks based clustering method (STSNN-DPC). STSNN-DPC represents spatio-temporal neighborhood correlation by constructing spatio-temporal shared nearest neighbors, which improves the accuracy of clustering results for spatio-temporal data. Specifically, we propose spatio-temporal shared nearest neighbors to capture the shared neighbors of spatio-temporal data in different dimensions. Based on this, we propose a multidimensional similarity metric to measure the local density and relative distance of spatio-temporal data points. Experimental results show that STSNN-DPC exhibits excellent clustering performance on multiple synthetic datasets and real pedestrian datasets, with an 8% improvement over state-of-the-art clustering algorithms. Clustering Spatio-temporal data Spatio-temporal shared nearest neighbors Density peaks Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 07 Nov, 2024 Submission checks completed at journal 07 Nov, 2024 First submitted to journal 03 Nov, 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. 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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-5381784","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":375124937,"identity":"df1c1d24-4d4a-44bf-861c-cac24950e2ed","order_by":0,"name":"Fengling Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACfmbmgw8/VNjIsTEcPkCcFsl2tmRjiTNpxvyMxxKI02JwnsdMgrflcOLM5jMGRLrsMI+BhGTD4cQNx858vPGGwU5Ot4GADsZmtgKDwh3pxhvOnN1sOYch2djsAAEtzMzMGxIkz1jLbrhxdps0D8OBxG2EtLAxMxgc4G1jZtxw/80z4rTwMLMYNvC2OSvObDjDRpwWCWa2ZGZwIDMcM7acY0CEX+zPHz7+ExqVD2+8qbCTI6gF1UoeYqMGSQupOkbBKBgFo2BEAABeEkYILam5MQAAAABJRU5ErkJggg==","orcid":"","institution":"Anhui Normal University","correspondingAuthor":true,"prefix":"","firstName":"Fengling","middleName":"","lastName":"Zhang","suffix":""},{"id":375124941,"identity":"56639919-5bcb-4e91-a7e8-b8af304e8c04","order_by":1,"name":"Shengqiang Huang","email":"","orcid":"","institution":"Anhui Normal University","correspondingAuthor":false,"prefix":"","firstName":"Shengqiang","middleName":"","lastName":"Huang","suffix":""},{"id":375124943,"identity":"5312c754-fdec-47a6-8dd7-cc46e8a43810","order_by":2,"name":"Haiyan Zhang","email":"","orcid":"","institution":"Anhui Normal University","correspondingAuthor":false,"prefix":"","firstName":"Haiyan","middleName":"","lastName":"Zhang","suffix":""},{"id":375124945,"identity":"3a4872e9-4374-4088-a7ec-fdfd9b75e9ea","order_by":3,"name":"Yonglong Luo","email":"","orcid":"","institution":"Anhui Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yonglong","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2024-11-03 11:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5381784/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5381784/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69823008,"identity":"820c5646-cc19-4fc0-81e8-fd7ba604b4a6","added_by":"auto","created_at":"2024-11-25 14:35:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1490337,"visible":true,"origin":"","legend":"","description":"","filename":"1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5381784/v1_covered_9a5aeae2-7f55-4c48-bf71-44aced53d837.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"STSNN-DPC: spatio-temporal shared nearest neighbors and density peaks based clustering method","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Clustering, Spatio-temporal data, Spatio-temporal shared nearest neighbors, Density peaks","lastPublishedDoi":"10.21203/rs.3.rs-5381784/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5381784/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDensity peaks clustering (DPC) algorithm is a novel clustering method, which offers advantages such as simple parameter adjustment and ease of implementation. 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