Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data

TLDR

Rapid advances in information technology and the internet have produced vast, diverse data sets, making data mining increasingly vital for extracting accurate information, especially in the growing domain of crime data where such events represent unwanted societal behavior. The study seeks to extract meaningful insights from crime data to enhance decision‑making. The article presents examples of data mining and machine learning in crime and security, offering a conceptual framework of big data, data mining, machine learning, and deep learning, including task types, processes, and methods.

Abstract

Along with the rapid change of information technologies and the widespread use of the internet over time, data stacks with ample diversity and quite large volumes has emerged. The use of data mining is increasing day by day due to the huge part it plays in the acquisition of information by making necessary analyses especially within a large amount of data. Obtaining accurate information is a key factor affecting decision-making processes. Crime data is included among the application areas of data mining, being one of the data stacks which is rapidly growing with each passing day. Crime events constitute unwanted behaviour in every society. For this reason, it is important to extract meaningful information from crime data. This article aims to provide an overview of the use of data mining and machine learning in crime data and to give a new perspective on the decision-making processes by presenting examples of the use of data mining for a crime. For this purpose, some examples of data mining and machine learning in crime and security areas are presented by giving a conceptual framework in the subject of big data, data mining, machine learning, and deep learning along with task types, processes, and methods.