Machine Learning Research

| Peer-Reviewed |

A Decision Tree Algorithm Based System for Predicting Crime in the University

Received: 27 January 2017    Accepted: 13 February 2017    Published: 02 March 2017
Views:       Downloads:

Share This Article

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.

DOI 10.11648/j.mlr.20170201.14
Published in Machine Learning Research (Volume 2, Issue 1, March 2017)
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), 2024. Published by Science Publishing Group

Keywords

Crime, Classification Rules, Data Mining, Decision Trees, ID3, Prediction, University

References
[1] Barnadas M. V. (2016), Machine Learning Applied to Crime Prediction, A Degree Thesis submitted to the Faculty of the Escola Tècnica d'Enginyeria de Telecomunicació de Barcelona, Universitat Politècnica de Catalunya.
[2] Berry, M. and Linoff, G. (1997). Data mining techniques: For marketing, sales and support. New York. John Wiley and Sons. Inc.
[3] Cowell, R. G., Dawid, A. P., Lauritzen, S. L. and Spiegelhalter, D. J., (1999). Probabilistic Networks and Expert Systems, Springer-Verlag.
[4] Dilek S., Çakır H. and Aydın M. (2015), Applications of Artificial Intelligence Techniques to Combating Cyber Crimes: a Review, International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 6, No. 1, pp 21-39.
[5] Jenson, F. V. (1996). An Introduction to Bayesian Networks. UCL Press.
[6] Marcus, S., Moy, M. & Coffman, T. (2007), Social Network Analysis, in Diane J. Cook and Lawrence B. Holder, ‘Mining Graph Data’, John Wiley & Sons.
[7] McClendon L. and Meghanathan N. (2015). Using Machine Learning Algorithms to Analyze Crime Data, Machine Learning and Applications: An International Journal (MLAIJ) Vol. 2, No. 1, Pp 1-12.
[8] Megaputer Intelligence, Inc. (2012). Crime Pattern Analysis: Megaputer Case Study. http://www.elon.edu/facstaff/mconklin/cis230/cases/crime_pattern_case.pdf.
[9] Oatley G. C., Ewart B. W and Zeleznikow J., (2004). Decision Support Systems for Police: Lessons from the Application of Data Mining Techniques to ‘Soft’ Forensic Evidence. Expert Systems with Applications.
[10] Pearl, J. (2000). Casualty: Models, Reasoning, and Inference. Cambridge University Press, UK.
[11] Ramasubbareddy, B., Govardhan, A., & Ramamohanreddy, A. (2011). Classification Based on Positive and Negative Association Rules. International Journal of Data Engineering (IJDE) Volume (2) Issue (2), Pp 84.
[12] Seifert, J. (2007). Data Mining: An overview. CRS Report for Congress.
[13] Surjeet Kumar Y., Saurabh P. (2012), “Data Mining Application in Enrollment Management: A Case Study “International Journal of Computer Applications (0975 – 8887), Volume 41–No. 5.
[14] Samrat S. & Vikesh K. (2012) “Classification of Student’s data Using Data Mining Techniques for Training & Placement Department in Technical Education” International Journal of Computer Science and Network (IJCSN) Volume 1, Issue 4.
[15] Thearling, K. (2003). Understanding Data Mining: It’s all in the Interaction. Available at www.thearling.com.
[16] Ye, F. (2011). Investigating the effects of sample size, model misspecification, and underreporting in crash data on three commonly used traffic crash severity models. Ph. D. Dissertation. Texas A&M University, UMI Number: 3471262.
[17] Tesfaye, H. (2002). Predictive Modeling Using Data Mining Techniques In Support to Insurance Risk Assessment. Masters Thesis Addis Ababa University, Addis Ababa.
[18] Witten I. H., & Frank E., (2000). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementation. Morgan Kaufmann, October 2000, ISBN 1-55860-552-5.
Author Information
  • Department of Computer Science, Redeemer’s University, Ede, Nigeria

  • Department of Computer Science, Federal University, Oye-Ekiti, Nigeria

  • Department of Computer Science, Redeemer’s University, Ede, Nigeria

Cite This Article
  • 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

    Copy | Download

    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

    Copy | Download

    AMA 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

    Copy | 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://download.sciencepg.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}
    }
    

    Copy | Download

  • 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  - 

    Copy | Download

  • Sections