A Pattern Growth-Based Approach for Mining Spatiotemporal Co-occurrence Patterns

Abstract

Spatiotemporal co-occurrence pattern (STCOP) mining refers to discovering the subsets of event types whose instances frequently co-locate in a spatial context and coincide in a temporal context. STCOP mining is the spatiotemporal extension to Frequent Itemset Mining (FIM). Unlike the classical FIM approaches, which are applied on transactional databases, STCOP mining is applied on the spatiotemporal datasets comprised of event instances which are represented by evolving region trajectories. Previous STCOP mining algorithms are Apriori-based, where the number of candidate patterns can grow exponentially with the number of event types. In this work, we present a pattern growth-based approach for mining STCOPs, which allows us to discover STCOPs without computationally expensive candidate generation processes. We experimented our algorithm with four real-life solar event datasets and compared its performance with the earlier Apriori-based approach.