The Application of Data Mining in Crime Prevention: The Case of Oromia Police Commission
| dc.contributor.advisor | Tadesse, Ethiopia (PhD) | |
| dc.contributor.advisor | Teferi, Dereje (PhD) | |
| dc.contributor.author | Woldu, Leul | |
| dc.date.accessioned | 2020-06-10T08:16:29Z | |
| dc.date.accessioned | 2023-11-18T12:45:36Z | |
| dc.date.available | 2020-06-10T08:16:29Z | |
| dc.date.available | 2023-11-18T12:45:36Z | |
| dc.date.issued | 2003-07 | |
| dc.description.abstract | Law enforcement agencies like that of police today are faced with large volume of data that must be processed and trans faced into useful intonation and hence data mining can greatly improve crime analysis and aid in reducing an d 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 n an d to build a model that could help to extract crime patterns. With this objective decisis on trees and neural network were employed to classify crime records on the basis of the values of attributes crime label (Crime Label) and crime scene (Selle Label). Results of the experiments have shown that decision tree ha s classified crime records at an accuracy rate of 94 percent when the attribute Crime Label is used as a basis for class fiction. 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 Scale Label decision tree h as shown an accuracy rat e 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. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/21497 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Information Science | en_US |
| dc.title | The Application of Data Mining in Crime Prevention: The Case of Oromia Police Commission | en_US |
| dc.type | Thesis | en_US |