Introduction of a Method to Choose the Number of Clusters of a Mixed Dataset under Kproto

Introduction of a Method to Choose the Number of Clusters of a Mixed Dataset under Kproto | 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 Introduction of a Method to Choose the Number of Clusters of a Mixed Dataset under Kproto Ahalya Sivathayalan, Kenneth Chu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6795375/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 Identifying homogenous subgroups within a dataset, particularly without prior knowledge of the number of such subgroups is of great interest to clinicians or policy makers as this knowledge aids them to tailor strategies to introduce interventions. Clustering is traditionally used to identify these subgroups. Partition clustering has been extensively used for its efficiency over other clustering methods, but it requires the number of clusters to be known in advance. While measures exist to estimate the number of clusters of a numeric dataset, no approaches exist in the literature to aid in selecting the number of clusters of a dataset that has both numeric and categorical variables. In this paper, we introduce and demonstrate a new method to select the number of clusters of a mixed dataset so that the clusters are stable, have maximized and stable categorical variable contribution using the extensively studied, Kproto clustering algorithm. Applied Statistics number of clusters Kproto mixed data partition clustering Full Text Additional Declarations The authors declare no competing interests. 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-6795375","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464768123,"identity":"5338b49e-d1fc-4e2d-a6fb-b416cd847015","order_by":0,"name":"Ahalya Sivathayalan","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-1635-8501","institution":"Statistics Canada","correspondingAuthor":true,"prefix":"","firstName":"Ahalya","middleName":"","lastName":"Sivathayalan","suffix":""},{"id":464768124,"identity":"01d1791e-165f-4f41-98b6-781f80f97096","order_by":1,"name":"Kenneth Chu","email":"","orcid":"","institution":"Statistics Canada","correspondingAuthor":false,"prefix":"","firstName":"Kenneth","middleName":"","lastName":"Chu","suffix":""}],"badges":[],"createdAt":"2025-06-01 11:28:05","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-6795375/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6795375/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83801845,"identity":"fc955bb7-a40d-4ddc-9c7c-c2bce7b56266","added_by":"auto","created_at":"2025-06-03 03:19:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1926094,"visible":true,"origin":"","legend":"","description":"","filename":"Introofnumberofclusterformixeddatakproto.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6795375/v1_covered_ad11bf4a-0fe9-42c4-b728-f0a2413781a9.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eIntroduction of a Method to Choose the Number of Clusters of a Mixed Dataset under Kproto\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"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":"number of clusters, Kproto, mixed data, partition clustering","lastPublishedDoi":"10.21203/rs.3.rs-6795375/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6795375/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIdentifying homogenous subgroups within a dataset, particularly without prior knowledge of the number of such subgroups is of great interest to clinicians or policy makers as this knowledge aids them to tailor strategies to introduce interventions. 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