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.
| Published in | Machine Learning Research (Volume 2, Issue 1) |
| DOI | 10.11648/j.mlr.20170201.14 |
| Page(s) | 26-34 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Crime, Classification Rules, Data Mining, Decision Trees, ID3, Prediction, University
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APA Style
Adewale Opeoluwa Ogunde, Gabriel Opeyemi Ogunleye, Oluwaleke Oreoluwa. (2017). A Decision Tree Algorithm Based System for Predicting Crime in the University. Machine Learning Research, 2(1), 26-34. https://doi.org/10.11648/j.mlr.20170201.14
ACS Style
Adewale Opeoluwa Ogunde; Gabriel Opeyemi Ogunleye; Oluwaleke Oreoluwa. A Decision Tree Algorithm Based System for Predicting Crime in the University. Mach. Learn. Res. 2017, 2(1), 26-34. doi: 10.11648/j.mlr.20170201.14
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Download@article{10.11648/j.mlr.20170201.14,
author = {Adewale Opeoluwa Ogunde and Gabriel Opeyemi Ogunleye and Oluwaleke Oreoluwa},
title = {A Decision Tree Algorithm Based System for Predicting Crime in the University},
journal = {Machine Learning Research},
volume = {2},
number = {1},
pages = {26-34},
doi = {10.11648/j.mlr.20170201.14},
url = {https://doi.org/10.11648/j.mlr.20170201.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170201.14},
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.},
year = {2017}
}
TY - JOUR T1 - A Decision Tree Algorithm Based System for Predicting Crime in the University AU - Adewale Opeoluwa Ogunde AU - Gabriel Opeyemi Ogunleye AU - Oluwaleke Oreoluwa Y1 - 2017/03/02 PY - 2017 N1 - https://doi.org/10.11648/j.mlr.20170201.14 DO - 10.11648/j.mlr.20170201.14 T2 - Machine Learning Research JF - Machine Learning Research JO - Machine Learning Research SP - 26 EP - 34 PB - Science Publishing Group SN - 2637-5680 UR - https://doi.org/10.11648/j.mlr.20170201.14 AB - 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. VL - 2 IS - 1 ER -
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