Quantum walk artificial bee colony algorithm hybrid K-modes for clustering

Quantum walk artificial bee colony algorithm hybrid K-modes for clustering | 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 Quantum walk artificial bee colony algorithm hybrid K-modes for clustering Gang Xu, Lefeng Wang, Yuwei Huang, Yong Lu, Xiu-Bo Chen, Zongpeng Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6537767/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Clustering is a crucial research direction in data mining. Data can be broadly classified into numerical and categorical types. The traditional K-modes algorithm is a simplistic yet effective approach for clustering categorical data. However, it has a propensity to become entrapped in local optima and is sensitive to the original cluster centers. This research introduces a quantum walk artificial bee colony hybrid K-modes clustering algorithm (QWABC-K) to overcome these restrictions. By leveraging the optimization results of this algorithm as the original cluster centers, the proposed approach mitigates the sensitivity of K-modes to original cluster centers, thereby enhancing clustering performance. First, a discrete quantum walk model with two goal nodes is designed to enhance the optimization efficiency of both employed and onlooker bees. Second, an updating strategy for adaptive rotation angles is introduced based on the quantum rotation gate. This strategy is applied during the scout bee phase and is combined with a global bootstrap factor to strengthen the quality of solutions. Third, an elite population grouping strategy is developed to dynamically adjust the population structure, enabling the population to evolve in different directions through targeted methods. Finally, experimental comparisons with other clustering algorithms are conducted on four famous UCI benchmark datasets to evaluate the effectiveness of the suggested QWABC-K algorithm. The results demonstrate that QWABC-K outperforms existing clustering algorithms in terms of clustering performance. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Nov, 2025 Reviews received at journal 05 Nov, 2025 Reviews received at journal 20 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviews received at journal 26 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers agreed at journal 10 Jun, 2025 Reviewers invited by journal 08 Jun, 2025 Editor assigned by journal 27 Apr, 2025 Submission checks completed at journal 27 Apr, 2025 First submitted to journal 26 Apr, 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. 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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-6537767","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469092294,"identity":"091bfc6d-229b-4963-8415-6f9138de6d9c","order_by":0,"name":"Gang Xu","email":"","orcid":"","institution":"North China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Xu","suffix":""},{"id":469092295,"identity":"c0c8a0fe-073f-4701-a247-351edd3238ea","order_by":1,"name":"Lefeng Wang","email":"","orcid":"","institution":"North China University of 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