Comparative Analysis of Certain Clustering Algorithms That Do Not Require Predefined Number of Clusters on the Articles of the Criminal Code of the Republic of Serbia - Raičević, Sanja; Nikolić, Vojkan (Journal of Computer and Forensic Sciences, 2024)
This paper explores the application of certain clustering algorithms for analyzing textual documents from the Criminal Code (CC) of the Republic of Serbia (RS). Clustering was performed using three popular algorithms: DBSCAN, Mean-Shift, and Hierarchical Clustering (Agglomerative Clustering). The input data consisted of textual documents in .txt format, where each document corresponds to a specific article of the law. The aim of this study is to identify thematic groups within the legislative texts and analyze the advantages and disadvantages of each algorithm in the context of the specific characteristics of legal documents. The clustering results using these three algorithms show that DBSCAN faces challenges with noise, while Mean-Shift effectively detects dense clusters, and Hierarchical Clustering allows for a detailed analysis at various levels of granularity. In conclusion, this paper provides valuable insights into the application of these clustering algorithms to legal texts and offers recommendations for their selection when analyzing similar datasets.