Detecting crime patterns from Swahili newspapers using text mining Online publication date: Sun, 03-Sep-2017
by George Matto; Joseph Mwangoka
International Journal of Knowledge Engineering and Data Mining (IJKEDM), Vol. 4, No. 2, 2017
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.
Online publication date: Sun, 03-Sep-2017
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Knowledge Engineering and Data Mining (IJKEDM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org