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Title	Analyzing Criminal Macrocauses On Intentional Lethal Violent Crimes: An Unsupervised Learning Approach For Smart City Initiatives
ID_Doc	9488
Authors	De Vasconcelos Dos Santos Junior R.; Venceslau Coelho J.V.; Azevedo Cacho N.A.; Amorim De Araujo D.S.
Year	2023
Published	Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023
DOI	http://dx.doi.org/10.1109/ISC257844.2023.10293658
Abstract	In Brazil, intentional lethal violent crimes pose challenges to public safety initiatives and smart cities. We conducted a study utilizing clustering techniques as part of unsupervised learning to analyze the criminal macrocauses of such crimes. We explored different clustering strategies to identify the optimal number of clusters to group the ILCV data of the state of Rio Grande do Norte. By grouping this criminal data and applying the Distortion, Silhouette, and Calinski-Harabasz scores, we reached an ideal number of two to three clusters. Our findings align with smart city initiatives and present an opportunity to integrate data-driven approaches, theory, and machine learning techniques into public safety measures. © 2023 IEEE.
Author Keywords	Clustering; Criminal Macrocauses Analysis; Intentional Lethal Violent Crimes; Machine Learning Unsupervised; Public Safety; Smart Cities

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