Risk and Pattern Analysis of Pakistani Crime Data Using Unsupervised Learning Techniques

Risk and Pattern Analysis of Pakistani Crime Data Using Unsupervised Learning Techniques

To reduce crime rates, there is a need to understand and analyse emerging patterns of criminal activities. This study examines the occurrence patterns of crimes using the crime dataset of Lahore, a metropolitan city in Pakistan. The main aim is to facilitate crime investigation and future risk analy...

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Detalles Bibliográficos
Autores principales: Faria Ferooz, Malik Tahir Hassan, Sajid Mahmood, Hira Asim, Muhammad Idrees, Muhammad Assam, Abdullah Mohamed, El-Awady Attia
Formato: Artículo
Lenguaje:English
Publicado: MDPI AG 2022-04-01
Colección:Applied Sciences
Materias:
crime analytics
clustering
risk identification
frequent pattern mining
data mining
public safety
Acceso en línea:https://www.mdpi.com/2076-3417/12/7/3675

Internet

https://www.mdpi.com/2076-3417/12/7/3675

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