Crime Analysis using Open Source Information

TL;DR

This paper introduces an unsupervised data mining approach using clustering and association techniques to analyze crimes from open source information, providing efficient and broad insights comparable to manual analysis.

Contribution

It presents a novel unsupervised method for crime analysis using open source data, improving efficiency and reducing manual effort.

Findings

The method is efficient and broadens understanding of crime patterns.

Results are comparable to manual analysis but faster.

The approach saves significant time in crime analysis.

Abstract

In this paper, we present a method of crime analysis from open source information. We employed un-supervised methods of data mining to explore the facts regarding the crimes of an area of interest. The analysis is based on well known clustering and association techniques. The results show that the proposed method of crime analysis is efficient and gives a broad picture of the crimes of an area to analyst without much effort. The analysis is evaluated using manual approach, which reveals that the results produced by the proposed approach are comparable to the manual analysis, while a great amount of time is saved.