A weighted k-mean clustering algorithm based on singular values with offset clustering centers

A weighted k-mean clustering algorithm based on singular values with offset clustering centers | 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 A weighted k-mean clustering algorithm based on singular values with offset clustering centers shaobo deng, xing lin, Weili Yuan, Zemin Liao, Sujie Guan, Min Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4762796/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 The K-means algorithm is widely used for dataset clustering, but it does not consider the importance of each attribute dimension when dealing with feature attributes and clustering center selection, but rather treats all attributes as having equal importance. In order to solve this problem, this paper proposes a weighted k-mean clustering algorithm (SVW-KMeans) based on singular values with offset clustering centers. The algorithm calculates the weight information of the data points through singular value decomposition to focus on the most significant and most different features, joining the weight calculation to optimize the objective function, and at the same time, the weighted arithmetic mean of the individuals is used as the clustering center, and the clustering center is shifted towards the high importance so as to take into full consideration of the importance of the different features in the clustering process. The experimental results show that the SVW-KMeans algorithm outperforms other algorithms in clustering on synthetic and real datasets, which verifies that the SVW-KMeans algorithm outperforms other mainstream clustering algorithms in terms of clustering quality and stability. K-means clustering algorithm center of clustering Feature weights singular value decomposition 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-4762796","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":341521020,"identity":"0ac80159-719d-43f4-8f0f-9757c825b912","order_by":0,"name":"shaobo deng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACAxBOALHYGxsOfKiQkJMnXgvP4YMPZ5yxMDZsIEILBEikJRvztlUkMhwgoMWcvceg4GHbYXlzhhwzCd55EgmMDcwPH93Ao8Wy54yBQWLbYcOdDWfMJCS3SeSxM7AZG+fgc9iNHLAWxg0He8wkDLdJFDM28LBJE6PFfsNhHjOJxDkSiQ0HiNSSuOEYW7LBwQZitJw5VmCQcC49ecMZ5oMPG45JGBs2E/LL8eZthj/KrG033H/YcPhPTZ2cPHvzw8f4tAABmwEjGzKfGb9ysJIHDH8IqxoFo2AUjIIRDAAHDFLu23UHYAAAAABJRU5ErkJggg==","orcid":"","institution":"Nanchang Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"shaobo","middleName":"","lastName":"deng","suffix":""},{"id":341521021,"identity":"0665a119-57c9-40b2-90bf-31fe1f3a2060","order_by":1,"name":"xing lin","email":"","orcid":"","institution":"Nanchang Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"xing","middleName":"","lastName":"lin","suffix":""},{"id":341521022,"identity":"ebca4f40-7fa3-42fc-a9b5-5b0932fdbaf5","order_by":2,"name":"Weili Yuan","email":"","orcid":"","institution":"Nanchang Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Weili","middleName":"","lastName":"Yuan","suffix":""},{"id":341521023,"identity":"adfa6ba1-1159-42f0-a23e-fe1e73350efa","order_by":3,"name":"Zemin Liao","email":"","orcid":"","institution":"Nanchang Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zemin","middleName":"","lastName":"Liao","suffix":""},{"id":341521024,"identity":"2ea7c4c2-b7ce-4f97-82b2-6830dd043aaa","order_by":4,"name":"Sujie Guan","email":"","orcid":"","institution":"Nanchang Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Sujie","middleName":"","lastName":"Guan","suffix":""},{"id":341521025,"identity":"40e2d51e-6639-4c10-876e-010bfd0b8387","order_by":5,"name":"Min Li","email":"","orcid":"","institution":"Nanchang Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-07-18 12:59:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4762796/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4762796/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66785289,"identity":"baaf44e4-ed1f-4cd7-84ba-84b580e30414","added_by":"auto","created_at":"2024-10-16 12:47:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":840894,"visible":true,"origin":"","legend":"","description":"","filename":"Aweightedkmeanclusteringalgorithmbasedonsingularvalueswithoffsetclusteringcenters.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4762796/v1_covered_1e80be8a-6654-4856-b509-3ef0cd102954.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A weighted k-mean clustering algorithm based on singular values with offset clustering centers","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":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":"K-means clustering algorithm, center of clustering, Feature weights, singular value decomposition","lastPublishedDoi":"10.21203/rs.3.rs-4762796/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4762796/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe K-means algorithm is widely used for dataset clustering, but it does not consider the importance of each attribute dimension when dealing with feature attributes and clustering center selection, but rather treats all attributes as having equal importance. In order to solve this problem, this paper proposes a weighted k-mean clustering algorithm (SVW-KMeans) based on singular values with offset clustering centers. The algorithm calculates the weight information of the data points through singular value decomposition to focus on the most significant and most different features, joining the weight calculation to optimize the objective function, and at the same time, the weighted arithmetic mean of the individuals is used as the clustering center, and the clustering center is shifted towards the high importance so as to take into full consideration of the importance of the different features in the clustering process. The experimental results show that the SVW-KMeans algorithm outperforms other algorithms in clustering on synthetic and real datasets, which verifies that the SVW-KMeans algorithm outperforms other mainstream clustering algorithms in terms of clustering quality and stability.\u003c/p\u003e","manuscriptTitle":"A weighted k-mean clustering algorithm based on singular values with offset clustering centers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-26 15:15:34","doi":"10.21203/rs.3.rs-4762796/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"4fe962db-e665-4914-9ddb-d31cf6251fa3","owner":[],"postedDate":"August 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-16T12:38:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-26 15:15:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4762796","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4762796","identity":"rs-4762796","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}