Using Apriori Algorithm Technique to Analyze Crime Patterns for Kenyan National Crime Data: A County Perspective
Edigar Adero, George Onyango Okeyo, Waweru R. Mwangi · 2020
The aim of this research is to identify approaches for using Association Rule Mining algorithms to discover rules from the Kenyan crime datasets at a county level. WEKA is the main tool that was used in this research. WEKA is a tool that provides a wide variety of machine learning algorithms that can be used in data mining. In this case the Apriori algorithm was applied on the dataset to discover frequent attribute sets and associations between them. The original Crime dataset was preprocessed to comprise data for 47 counties each having 7 attributes of interest. This was run through WEKA to discover the best Association Rules. These discovered rules could in turn be used to determine the Crime risk factor of a particular county.