Density Peaks Clustering Algorithm Based on Natural Neighbor and Multi-Cluster Merging Strategy

Density Peaks Clustering Algorithm Based on Natural Neighbor and Multi-Cluster Merging Strategy | 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 Density Peaks Clustering Algorithm Based on Natural Neighbor and Multi-Cluster Merging Strategy Fang Wan, Lili Wei, Chao Shi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7927835/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 19 You are reading this latest preprint version Abstract DPC(Clustering by fast search and find of Density Peaks) is a simple and effective density-based clustering algorithm that requires few parameters and does not involve iterative processing. However, it also has limitations, such as sensitivity to the selection of the cutoff distance parameter \({d_c}\) , and the identification of cluster centers from the decision graph involves subjectivity. Moreover, DPC demonstrates suboptimal performance on datasets with multi-density manifold structures. To address these limitations, a density peaks clustering algorithm based on natural neighbor and multi-cluster merging strategy (DPC-NaN-MS) has been proposed. Firstly, DPC-NaN-MS adaptively identifies the natural neighbor set of each data point and refines local density by incorporating geodesic distance, thereby mitigating the impact of \({d_c}\) and enhancing clustering performance on datasets with uneven density distributions. Secondly, initial subclusters are formed by searching for natural local density peaks. A novel subcluster merging strategy is introduced, which progressively intrgrates subclusters until the predefined number of clusters is reached. Experimental results on manifold datasets with uneven density distributions and complex morphologies, as well as on real-world datasets, fully demonstrate the effectiveness and superiority of DPC-NaN-MS. clustering analysis density peaks clustering natural neighbor multi-cluster merging strategy natural local density peak Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Dec, 2025 Reviews received at journal 24 Dec, 2025 Reviews received at journal 21 Dec, 2025 Reviews received at journal 20 Dec, 2025 Reviews received at journal 20 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviewers agreed at journal 14 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviewers agreed at journal 09 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers invited by journal 08 Dec, 2025 Editor assigned by journal 08 Dec, 2025 Submission checks completed at journal 29 Oct, 2025 First submitted to journal 22 Oct, 2025 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-7927835","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558861995,"identity":"6bb416d6-c8ba-48d5-8886-9ac7ead420d4","order_by":0,"name":"Fang Wan","email":"","orcid":"","institution":"Ningxia University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Wan","suffix":""},{"id":558861996,"identity":"50cce051-574c-4af0-9174-5ad9fa27c71d","order_by":1,"name":"Lili 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However, it also has limitations, such as sensitivity to the selection of the cutoff distance parameter\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({d_c}\\)\u003c/span\u003e\u003c/span\u003e, and the identification of cluster centers from the decision graph involves subjectivity. Moreover, DPC demonstrates suboptimal performance on datasets with multi-density manifold structures. To address these limitations, a density peaks clustering algorithm based on natural neighbor and multi-cluster merging strategy (DPC-NaN-MS) has been proposed. Firstly, DPC-NaN-MS adaptively identifies the natural neighbor set of each data point and refines local density by incorporating geodesic distance, thereby mitigating the impact of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({d_c}\\)\u003c/span\u003e\u003c/span\u003eand enhancing clustering performance on datasets with uneven density distributions. Secondly, initial subclusters are formed by searching for natural local density peaks. A novel subcluster merging strategy is introduced, which progressively intrgrates subclusters until the predefined number of clusters \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003eis reached. Experimental results on manifold datasets with uneven density distributions and complex morphologies, as well as on real-world datasets, fully demonstrate the effectiveness and superiority of DPC-NaN-MS.\u003c/p\u003e","manuscriptTitle":"Density Peaks Clustering Algorithm Based on Natural Neighbor and Multi-Cluster Merging Strategy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-12 20:17:56","doi":"10.21203/rs.3.rs-7927835/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-25T22:21:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-24T20:41:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-22T02:17:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-20T14:46:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-20T11:21:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207019917830049770294270636845258558684","date":"2025-12-16T00:22:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129008946321218097237687319859678874403","date":"2025-12-14T09:27:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172565224074942195396057868813746160611","date":"2025-12-11T13:24:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242333041378776064812298471569663833987","date":"2025-12-11T02:48:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278913812600158402428491085716914174325","date":"2025-12-10T11:30:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130322293807592123396682667892070890281","date":"2025-12-09T05:25:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33416330192524853655570708836034625314","date":"2025-12-09T03:22:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269305420479608661767900771663086565482","date":"2025-12-09T02:56:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92425925178021342851531985039478685923","date":"2025-12-09T02:42:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106730730147290845055951916269248229692","date":"2025-12-09T02:28:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-09T02:24:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-09T02:15:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-29T14:56:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Journal of Supercomputing","date":"2025-10-23T03:21:01+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"727f7e52-a076-4c5e-8763-4f0a2efe568b","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T23:08:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-12 20:17:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7927835","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7927835","identity":"rs-7927835","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}