Title: Detecting crime patterns from Swahili newspapers using text mining

Authors: George Matto; Joseph Mwangoka

Addresses: School of Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, Tanzania ' School of Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, Tanzania

Abstract: The Tanzania Police Force, as many other law enforcement agencies in developing countries, relies mostly on manual, personal judgments, and other inadequate tools for analysis of data in its crime databases. This approach is inadequate and prone to errors. Moreover, research shows that more than half of all crimes committed in Tanzania are not reported to police and thus it is likely that they are not analysed by the police. In this study, we use text mining to extract crime patterns from sources of crime data outside police databases. In fact, we use four daily published Swahili newspapers. With the help of our developed patterns mining model we extracted several crimes reported in the newspapers, we mapped the distribution of the mined crimes country-wide, and with the use of FP-growth, we generated association rules between the mined crimes. Results from this study will contribute to crime detection and prevention strategies.

Keywords: crime; crime patterns; text mining; association rules; FP-growth.

DOI: 10.1504/IJKEDM.2017.086244

International Journal of Knowledge Engineering and Data Mining, 2017 Vol.4 No.2, pp.145 - 156

Available online: 16 Aug 2017 *

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