A novel HP-Growth model for enhancing community policing in South Africa

South Africa (SA) grapples with a rising crime rate, which poses challenges to the 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. According to the study, HP-Growth outperformed the other two models on sparse datasets, whereas Hmine excelled on dense datasets. FP-Growth uses more memory and has a greater time complexity than HP-Growth and Hmine. The most suitable model was then integrated into the developed crime support system. The research outcome has practical implications for law enforcement in aiding strategic resource allocation and proactive policing for crime reduction and control.

• Publication year

  2025

• Venue

  Discover Computing

• Publication date

  2025-04-15

• Fields of study

  Law, Sociology, Computer Science, Political Science

• Identifiers
  DOI 10.1007/s10791-025-09529-x
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Using Remote Sensing Data in Urban Crime Analysis: A Systematic Review of English-Language Literature from 2003 to 2023

  2024influential reference
• A related degree-based frequent pattern mining algorithm for railway fault data

  2024cited by this paper
• The Role and Impact of Forensic Investigations Unit in the Fight Against Fraud, Corruption, Irregularities, Financial Misconduct and Maladministration in the Public Sector of South Africa

  2023cited by this paper
• CrimeTracker: A Frequent Pattern-based Crime Knowledge Support for Strategic Community Policing

  2023cited by this paper
• Edge Assisted Crime Prediction and Evaluation Framework for Machine Learning Algorithms

  2022cited by this paper
• Review of Shortest Path Algorithm

  2022cited by this paper
• LCCspm: l-Length Closed Contiguous Sequential Patterns Mining Algorithm to Find Frequent Athlete Movement Patterns from GPS

  2021cited by this paper
• An Improved Evaluation Methodology for Mining Association Rules

  2021cited by this paper
• South Africa Crime Visualization, Trends Analysis, and Prediction Using Machine Learning Linear Regression Technique

  2021cited by this paper
• Minimum threshold determination method based on dataset characteristics in association rule mining

  2021influential reference
• Using Apriori Algorithm Technique to Analyze Crime Patterns for Kenyan National Crime Data: A County Perspective

  2020influential reference
• A Review on Outlier/Anomaly Detection in Time Series Data

  2020cited by this paper
• Sampling Methods

  2020cited by this paper
• Comparative evaluation of pattern mining techniques: an empirical study

  2020cited by this paper
• A spatial analytical approach to assess the impact of a casino on crime: An example of JACK Casino in downtown Cincinnati

  2020influential reference
• The Use of Data Mining Techniques in Crime Prevention and Prediction

  2019cited by this paper
• A Theory-Driven Algorithm for Real-Time Crime Hot Spot Forecasting

  2019cited by this paper
• Hot Spots of Crime Are Not Just Hot Spots of Crime: Examining Health Outcomes at Street Segments

  2019influential reference
• Implementation of Apriori Algorithm for Analysis of Consumer Purchase Patterns

  2019cited by this paper
• Comparison of Priori and FP-Growth Algorithms in Determining Association Rules

  2019cited by this paper
• The Socio-Economic Determinants of Crime in South Africa: An Empirical Assessment

  2017influential reference
• Comparing dataset characteristics that favor the Apriori, Eclat or FP-Growth frequent itemset mining algorithms

  2016cited by this paper
• Information Technology and Quantitative Management ( ITQM 2015 ) A Revised Frequent Pattern Model for Crime Situation Recognition Based on Floor-Ceil Quartile Function

  2015cited by this paper
• Research of Improved FP-Growth Algorithm in Association Rules Mining

  2015cited by this paper
• Early Warning System for Temporary Crime Hot Spots

  2015influential reference
• Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets

  2014cited by this paper
• Compact Structure of Felonious Crime Sets Using FP-Tree Comparable Algorithms Analysis

  2014cited by this paper
• Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey

  2014cited by this paper
• Comparing the Performance of Frequent Pattern Mining Algorithms

  2013cited by this paper
• Efficient frequent pattern knowledge for crime situation recognition in developing countries

  2013cited by this paper
• Analysis of the Consecutive Mean Excision Algorithms

  2010cited by this paper
• Environmental criminology and crime analysis

  2009influential reference
• Quantitative Trading: How to Build Your Own Algorithmic Trading Business

  2008cited by this paper
• H-Mine: Fast and space-preserving frequent pattern mining in large databases

  2007influential reference
• Association Analysis: Basic Concepts and Algorithms

  2005influential reference
• Apriori, A Depth First Implementation

  2003cited by this paper
• H-mine: hyper-structure mining of frequent patterns in large databases

  2001influential reference
• Mining association rules between sets of items in large databases

  1993cited by this paper
• Human ecology and crime: A routine activity approach

  1980cited by this paper
• AN ANDROID LOCATION-BASED CRIME REPORTING SYSTEM USING THE GOOGLE MAP API

  year unknowncited by this paper

• No citing papers are available for this paper.