HP-Growth: A Novel Approach of Frequent Pattern-Based Model for Community Policing in South Africa

HP-Growth: A Novel Approach of Frequent Pattern-Based Model for Community Policing in South Africa | 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 HP-Growth: A Novel Approach of Frequent Pattern-Based Model for Community Policing in South Africa Apiwe Macingwane, Omowunmi E. Isafiade This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5023485/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Apr, 2025 Read the published version in Discover Computing → Version 1 posted 16 You are reading this latest preprint version Abstract South Africa (SA) grapples with a rising crime rate, which poses challenges to safety and economic growth of the country. Despite the limited literature on pattern-based models in SA, frequent pattern-based models, particularly Frequent Pattern Growth (FP-Growth) and Hyper Structure Mining (Hmine), have demonstrated utility in various research fields. This paper introduces a novel model, Hybrid Pattern-Growth (HP-Growth), which combines the strengths of FP-Growth and Hmine. A comparative analysis of the South African crime statistics (Stats SA crime) dataset’s computational time complexity, scalability, and memory usage revealed that HP-Growth and Hmine outperform FP-Growth. This study establishes association rule thresholds and emphasizes the importance of selecting the most appropriate pattern-based model for generating crime patterns. The most suitable model was then integrated into the developed crime support. The research outcomes can aid law enforcement in strategic resource allocation for addressing crime challenges in SA. Pattern-Based Model HP-Growth Association Rule Crime Control. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Apr, 2025 Read the published version in Discover Computing → Version 1 posted Editorial decision: Revision requested 21 Oct, 2024 Reviews received at journal 09 Oct, 2024 Reviews received at journal 08 Oct, 2024 Reviews received at journal 06 Oct, 2024 Reviewers agreed at journal 04 Oct, 2024 Reviewers agreed at journal 04 Oct, 2024 Reviewers agreed at journal 04 Oct, 2024 Reviews received at journal 01 Oct, 2024 Reviews received at journal 30 Sep, 2024 Reviewers agreed at journal 25 Sep, 2024 Reviewers agreed at journal 24 Sep, 2024 Reviewers agreed at journal 18 Sep, 2024 Reviewers invited by journal 18 Sep, 2024 Editor assigned by journal 17 Sep, 2024 Submission checks completed at journal 12 Sep, 2024 First submitted to journal 03 Sep, 2024 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. 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