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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/1019

Authors: WOLDU, LEUL
Advisors: W/t Ethiopia Tadesse
Ato Dereje Teferi
Copyright: 2003
Date Added: 9-May-2008
Publisher: Addis Ababa University
Abstract: Law enforcement agencies like that of police today are faced with large volume of data that must be processed and transformed into useful information and hence data mining can greatly improve crime analysis and aid in reducing and preventing crime. The purpose of this study is to explore the applicability of data mining technique in the efforts of crime prevention with particular emphasis to the Oromia Police Commission and to build a model that could help to extract crime patterns. With this objective decision trees and neural network were employed to classify crime records on the basis of the values of attributes crime label (CrimeLabel) and crime scene (SceneLabel). Results of the experiments have shown that decision tree has classified crime records at an accuracy rate of 94 percent when the attribute CrimeLabel is used as a basis for classification. Where as, in the same experiment, the accuracy rate of neural networks is 92.5 percent. On the other hand, in the case of classification of records on the values of the attribute SceneLabel decision tree has shown an accuracy rate of 85 percent while neural network revealed 80 percent. In both experiments the output indicated that decision tree performed better. Besides, decision tree generated understandable rules that could be easily presented in human language and thus police officers can make use of these rules for designing crime prevention strategies. Thus, this experiment has proved that data mining is valuable to support the crime prevention process and particularly, decision trees seem more appropriate for the domain problem.
Description: A thesis submitted to the School of Graduate Studies of Addis Ababa University in partial fulfillment of the requirements for the Degree of Master of Science in Information Science.
URI: http://hdl.handle.net/123456789/1019
Appears in:Thesis - Information Science

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