Crimes have been the most dangerous threat to peace, development, human right, social, political and economic stability in Kenya. There is a great need to eradicate crime to facilitate development and counter all vices that are caused by crime. Efficient management of crime requires an adequate understanding of the patterns in which crime occur to put the appropriate measures in place for crime prevention. Crime has been in existence since the beginning of time hence will remain, and one of the solutions is to identify the pattern in which it occurs to prevent or counter it effectively as it occurs. The main objective of the study was to find out how different crimes are related. The study considered a number of data mining techniques which included; clustering, specifically k-means algorithm, mapping and APRIORI algorithm to analyze how different crimes are related and how often they occur. Crime cases were found to be decreasing over the years under study and counties with a high population reported higher number of crimes as compared to those with low population. The study suggested that these crimes could be controlled by directing more resources in the highly populated counties. The study leaves a research gap where the same crime data could be analyzed using time series methods since observed crime offenses are recorded alongside the time they occur.
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