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|Title: ||THE APPLICATION OF DATA MINING IN CRIME PREVENTION: THE CASE OF OROMIA POLICE COMMISSION|
|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.|
|Appears in:||Thesis - Information Science|
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