Exploration of Massive Crime Data Sets through Data Mining Techniques

Abstract

We incorporate two data mining techniques, clustering and association-rule mining, into a fruitful exploratory tool for the discovery of spatio-temporal patterns in data-rich environments. This tool is an autonomous pattern detector that efficiently and effectively reveals plausible cause–effect associations among many geographical layers. We present two methods for exploratory analysis and detail algorithms to explore massive databases. We illustrate the algorithms with real crime data sets.