Software Implementation and Comparative Analysis of Algorithms for Clustering Data on Criminal Activities

Keywords:
Clustering, Criminal Activities, Software Implementation, Time Series, Unsupervised Learning

Abstract:
This study investigates the application of clustering algorithms in crime data analysis, with a special emphasis on their software implementation and interpretation within security-oriented systems. A comparative analysis of four widely used clustering algorithms (K-Means, Agglomerative Hierarchical Clustering, Gaussian Mixture Models, and DBSCAN) is conducted to determine the most suitable method and the optimal number of clusters. The selected procedures are applied to bivariate time series of real-world criminal activities, with a focus on clustering performance, interpretability, and identification of significant patterns. The resulting clusters are interpreted as different regime states of the observed system, providing insight into temporal variations in criminal activities. The results obtained thus indicate that clustering methods can serve not only as descriptive tools, but also as a valuable analytical component in security systems and decision support frameworks.

CITATION:

IEEE format

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M. Petrić, V. Stojanović, “Software Implementation and Comparative Analysis of Algorithms for Clustering Data on Criminal Activities,” in Sinteza 2026 - International Scientific Conference on Information Technology, Computer Science, and Data Science, Belgrade, Singidunum University, Serbia, 2026, pp. 577-584. doi:10.15308/Sinteza-2026-577-584

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Petrić, M., Stojanović, V. (2026). Software Implementation and Comparative Analysis of Algorithms for Clustering Data on Criminal Activities. Paper presented at Sinteza 2026 - International Scientific Conference on Information Technology, Computer Science, and Data Science. doi:10.15308/Sinteza-2026-577-584

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