Research on Modeling of Government Debt Risk Comprehensive Evaluation Based on Multidimensional Data Mining

Research on Modeling of Government Debt Risk Comprehensive Evaluation Based on Multidimensional Data Mining | 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 Research on Modeling of Government Debt Risk Comprehensive Evaluation Based on Multidimensional Data Mining Li ChaoYing, Wu Xiang Da, Zhao En Hui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-852683/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Dec, 2021 Read the published version in Soft Computing → Version 1 posted 3 You are reading this latest preprint version Abstract In order to solve the problems of low accuracy of data mining, high relative error rate of evaluation and long time of evaluation in traditional government debt risk evaluation methods, this paper proposes a modeling method of government debt risk comprehensive evaluation based on multidimensional data mining. The MAFIA algorithm is used for multidimensional mining of government debt risk data, and K-means clustering algorithm is used for clustering processing of mined data. According to the clustering results, the KMV model is constructed, and the uncertainty factor is used to modify the model, so as to realize the comprehensive evaluation of government debt risk by using the modified KMV model. The experimental results show that the accuracy rate of government debt risk data mining is always above 91%, the relative error rate of evaluation is always below 3.4%, and the average evaluation time is 0.71s, the practical application effect is good. Geometry Topology Theoretical Computer Science Multidimensional data mining Government debt risk Comprehensive evaluation Evaluation modeling KMV model Full Text Cite Share Download PDF Status: Published Journal Publication published 01 Dec, 2021 Read the published version in Soft Computing → Version 1 posted Reviews received at journal 05 Sep, 2021 Reviewers invited by journal 04 Sep, 2021 First submitted to journal 26 Aug, 2021 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. 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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-852683","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":50195901,"identity":"150f6070-8e87-49ec-a273-ff2fef184d37","order_by":0,"name":"Li 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