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Detecting crime patterns from Swahili newspapers using text mining

Show simple item record Matto, George Mwangoka, Joseph 2023-03-22T05:52:58Z 2023-03-22T05:52:58Z 2017
dc.identifier.citation Matto, G. and Mwangoka, J. (2017). Detecting crime patterns from Swahili newspapers using text mining. Int. J. Knowledge Engineering and Data Mining, 4(2), 145–156. en_US
dc.description.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 analyzed 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. en_US
dc.language.iso en en_US
dc.publisher Inderscience Enterprises Ltd. en_US
dc.relation.ispartofseries 4;2
dc.subject Crime en_US
dc.subject Crime patterns en_US
dc.subject Text mining en_US
dc.subject Association rules en_US
dc.subject FP-growth en_US
dc.title Detecting crime patterns from Swahili newspapers using text mining en_US
dc.type Article en_US

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