Discovery of crime event sequences with constricted spatio-temporal sequential patterns

Abstract In this article, we introduce a novel type of spatio-temporal sequential patterns called Constricted Spatio-Temporal Sequential (CSTS) and thoroughly analyze their properties. We demonstrate that the set CSTS is concise representation all can be discovered in given dataset. To measure significance adapt participation index measure. also provide CSTS-Miner : an algorithm discovers strong event data. experimentally evaluate proposed algorithms using two crime-related datasets: Pittsburgh Police Incident Blotter Dataset Boston Crime Reports Dataset. experiments, compared with other four state-of-the-art algorithms: STS-Miner, CSTPM, STBFM CST-SPMiner. As results experiments suggest, much fewer than selected algorithms. Finally, examples interesting by algorithm.