An Ensemble Clustering Method Based on Several Different Clustering Methods

An Ensemble Clustering Method Based on Several Different Clustering Methods | 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 Short Report An Ensemble Clustering Method Based on Several Different Clustering Methods Sadegh Rezaei, Razieh Malekhosseini, S. Hadi Yaghoubyan, Karamollah Bagherifard, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4362549/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 As an unsupervised learning method, clustering is done to find natural groupings of patterns, points, or objects. In clustering algorithms, an important problem is the lack of a definitive approach based on which users can decide which clustering method is more compatible with the input data set. This problem is due to the use of special criteria for optimization. Cluster consensus, as the reuse of knowledge, provides a solution to solve the inherent challenges of clustering. Ensemble clustering methods have come to the fore with the slogan that combining several weak models is better than a strong model. This paper proposed the optimal K-Means Clustering Algorithm (KMCE) method as an ensemble clustering method. This paper has used the K-Means weak base clustering method as base clustering. Also, by adopting some measures, the diversity of the consensus has increased. The proposed ensemble clustering method has the advantage of K-Means, which is its speed. Also, it does not have its major weakness, which is the inability to detect non-spherical and non-uniform clusters. In the experimental results, we meticulously evaluated and compared the proposed hybrid clustering algorithm with other up-to-date and powerful clustering algorithms on different data sets, ensuring the robustness and reliability of our findings. The experimental results indicate the superiority of the proposed hybrid clustering method over other clustering algorithms in terms of F1-score, Adjusted rand index, and Normal mutual information. Ensemble clustering K-Means Clustering viability KMCE 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-4362549","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":298333176,"identity":"f2b9a353-61a6-4794-aae9-36069e9a6412","order_by":0,"name":"Sadegh Rezaei","email":"","orcid":"","institution":"Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"Sadegh","middleName":"","lastName":"Rezaei","suffix":""},{"id":298333177,"identity":"82eafc2e-fdfc-4e7c-9490-d918e0e6d727","order_by":1,"name":"Razieh Malekhosseini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie3QsWrDMBCA4RMesqTNnLdQKQgKxnqQLhICd8obZJAxKFPJWj9EIVlKhg5nBO1iqtWQpVo6u2O3KkkpZLDTboXqH8wh/HG2AGKxPxjFRJNChykBQIA0TKTAIXKF5IjkO6IHCf8mh+z+OUjYc134apPyyeisRvLosvuFDVvm6XUvaWR5sW5yWZXnAuFtqx4aGchTPtN9BKWZemMFtWOKgFsVTnT4WttLqPOLj0A4P5AXxZw/QVppyNpYskr2BDPWntrS+nJamVze7bYIVIK1YYsY+BfqVP1+a8KNLZvLrsOMM3fjX7t52kuOEwBSfw0/j//m5VgsFvsffQJxkHASY5KSoQAAAABJRU5ErkJggg==","orcid":"","institution":"Islamic Azad University","correspondingAuthor":true,"prefix":"","firstName":"Razieh","middleName":"","lastName":"Malekhosseini","suffix":""},{"id":298333178,"identity":"aa0c2a9d-9240-46f9-a1ae-203e55f24e62","order_by":2,"name":"S. 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In clustering algorithms, an important problem is the lack of a definitive approach based on which users can decide which clustering method is more compatible with the input data set. This problem is due to the use of special criteria for optimization. Cluster consensus, as the reuse of knowledge, provides a solution to solve the inherent challenges of clustering. Ensemble clustering methods have come to the fore with the slogan that combining several weak models is better than a strong model. This paper proposed the optimal K-Means Clustering Algorithm (KMCE) method as an ensemble clustering method. This paper has used the K-Means weak base clustering method as base clustering. Also, by adopting some measures, the diversity of the consensus has increased. The proposed ensemble clustering method has the advantage of K-Means, which is its speed. Also, it does not have its major weakness, which is the inability to detect non-spherical and non-uniform clusters. 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