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Data mining technique to analyse and predict crime using crime categories and arrest records

journal contribution
posted on 06.09.2021, 04:33 by Most Rokeya Khatun, Safial Islam Ayon, Md Rahat HossainMd Rahat Hossain, Mohammed Jaber Alam
Generally, crimes influence organisations as it starts occurring frequently in society. Because of having many dimensions of crime data, it is difficult to mine the available information using off the shelf or statistical data analysis tools. Improving this process will aid the police as well as crime protection agencies to solve the crime rate in a faster period. Also, criminals can often be identified based on crime data. Data mining includes strategies at the convergence of machine learning and database frameworks. Using this concept, we can extract previously unknown useful information and their patterns of occurrence from unstructured data. The sole purpose of this paper is to give an idea of how data mining can be utilised by crime investigation agencies to discover relevant precautionary measures from prediction rates. Data sets are analysed by some supervised classification algorithms, namely decision tree, K-nearest neighbours (KNN) and random forest algorithms. Crime forecasting is done for frequently occurring crimes like robbery, assault, theft, etc. Specifically, the results indicate the superiority of the random forest algorithm in test accuracy.

History

Volume

22

Issue

2

Start Page

444

End Page

452

Number of Pages

9

eISSN

2502-4760

ISSN

2502-4752

Publisher

Institute of Advanced Engineering and Science (IAES)

Additional Rights

CC BY-SA 4.0

Peer Reviewed

Yes

Open Access

Yes

Acceptance Date

21/03/2021

Era Eligible

Yes

Journal

Indonesian Journal of Electrical Engineering and Computer Science