Home > Archive > 2019 > Volume 9 Number 4 (Aug. 2019) >
IJMLC 2019 Vol.9(4): 533-538 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.4.837

Applying Data Mining Techniques in Predicting Index and non-Index Crimes

Allemar Jhone P. Delima

Abstract—An increasing incidence of crime has led to the development and use of computer-aided diagnosis system, tools and methods in analyzing, classifying and predicting crimes. This paper clusters municipalities in Surigao del Norte using K-Means algorithm. This is instrumental in finding identical traits, patterns and values in categorizing municipalities with much, more, and most number of recorded index and non-index crimes from 2013-2017. Prediction of its occurrence for the year 2018-2022 was also provided using ARIMA(1,0,7) model. Results showed that Surigao City has the most number of recorded index and non-index crimes. Followed by the municipality of Placer, Claver, and Dapa of cluster 2 and Del Carmen of cluster 3. Further, physical injury, homicide, violation of special laws, car napping, reckless imprudence resulting to physical injury, and other non-index crimes has 26%, 25%, 25%, 24%, 24%, and 23% forecasted increase for the year 2018-2022 with the highest occurrence in years 2018, 2018, 2020, 2018, 2020, and 2020 respectively. Future researchers may utilize other data mining techniques supported by a better accuracy result.

Index Terms—Crime forecasting, prediction, arima, data mining, crime mining.

A. J. Delima is with the College of Engineering and Information Technology, Surigao State College of Technology, Surigao City, 8400, Philippines (e-mail: allemarjpd@ssct.edu.ph).


Cite: Allemar Jhone P. Delima, "Applying Data Mining Techniques in Predicting Index and non-Index Crimes," International Journal of Machine Learning and Computing vol. 9, no. 4, pp. 533-538, 2019.

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

Article Metrics in Dimensions