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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\u2019s 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. 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