Crime Profiling and Pattern Detection Using MLA Tools: A Review

Abstract

The application of Machine Learning Algorithm (MLA) in performing statistics and modeling to reveal pattern of current and future behavior is significantly useful as to cope up with the recent evolution of Big Data environment. In crime investigation, data mining is an important tool used to uncover hidden information from a large amount of data. There are several approaches and techniques of data mining discussed in this paper in regard to managing crime dataset and analytic in criminology world. This study aims to describe the recent method and the most commonly model used in performing the crime-matching process by highlighting a number of important directions for future research to assist in the process of identifying patterns of crime. This paper explores the application of MLA to find the crime pattern by comparing all types of crime with different profiling such as date, time, and location of the crime and these are commonly known as an attributes, variables or parameters. As a result, this study observed that crime location and time are the most important profiling variables in criminological based analysis. This study also suggested that the combination of both clustering and classification techniques is best applied in such context.