Clustering-Assisted Regional Spatio-Temporal Sequence Pattern Mining in Crime Database

Abstract

With the recent advances in IoT and other smart devices, an explosive amount of data, both spatially and temporally significant are generated. Discovering interesting or useful patterns from these spatiotemporal data is referred to as spatiotemporal data mining. These patterns could be unordered, totally ordered or partially ordered based on the temporal ordering. This work focusses on the totally ordered patterns or sequential patterns from spatiotemporal event database. Spatiotemporal event sequence miner finds sequence of events that overlaps spatially and temporally. Traditional approaches discover patterns that are frequent in the entire dataset. In this work a clustering-assisted approach to find regionally or locally frequent spatiotemporal pattern is proposed. The proposed Clustering assisted Regional Spatiotemporal Event Sequence (CReST) mining approach overcomes the bias caused by uneven distribution of spatiotemporal events while mining patterns. Chicago crime dataset is used for evaluating the proposed approach with traditional sequence mining algorithm.