Volume 2, Issue 1, March 2017, Page: 26-34
A Decision Tree Algorithm Based System for Predicting Crime in the University
Adewale Opeoluwa Ogunde, Department of Computer Science, Redeemer’s University, Ede, Nigeria
Gabriel Opeyemi Ogunleye, Department of Computer Science, Federal University, Oye-Ekiti, Nigeria
Oluwaleke Oreoluwa, Department of Computer Science, Redeemer’s University, Ede, Nigeria
Received: Jan. 27, 2017;       Accepted: Feb. 13, 2017;       Published: Mar. 2, 2017
DOI: 10.11648/j.mlr.20170201.14      View  1393      Downloads  70
Abstract
CRIME is one of the major problems encountered in any society and universities together with other higher institutions of learning are not exceptions. Thus, there is an urgent need for security agents and agencies to battle and eradicate crime. The Directorate of Students and Services Development (DSSD) are responsible for investigating and detecting criminals of any crime committed within the Redeemer’s University. DSSD faces major challenges when it comes to detecting the real perpetrators of several crimes. An improvement in their strategy can produce positive results and high success rates, which is the basic objective of this project. Several methods have been applied to solve similar problems in the literature but none was tailored to solving the problem in Redeemer’s University and other universities. This work therefore applied classification rule mining method to develop a system for detecting crimes in universities. Past data for both crimes and criminals were collected from DSSD. In order to develop and test the proposed model, the data was pre-processed to get clean and accurate data. The Iterative Dichotomiser 3 (ID3) decision tree algorithm obtained from WEKA mining software was used to analyze and train the data. The model obtained was then used to develop a system that showed the hidden relationships between the crime-related data, in form of decision trees. This result was then used as a knowledge base for the development of the crime prediction system. The developed system could effectively predict a list of possible suspects by simply analyzing data retrieved from the crime scene with already existing data in the database. This system has all the potentials of helping the students’ affairs department and security apparatus of any university and other institutions to quickly detect either the real or possible perpetrators of crimes in the system.
Keywords
Crime, Classification Rules, Data Mining, Decision Trees, ID3, Prediction, University
To cite this article
Adewale Opeoluwa Ogunde, Gabriel Opeyemi Ogunleye, Oluwaleke Oreoluwa, A Decision Tree Algorithm Based System for Predicting Crime in the University, Machine Learning Research. Vol. 2, No. 1, 2017, pp. 26-34. doi: 10.11648/j.mlr.20170201.14
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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