Efficient Discovery of Sequential Patterns from Event-Based Spatio-Temporal Data by Applying Microclustering Approach

Abstract

Discovering various types of frequent patterns in spatiotemporal data is gaining attention of researchers nowadays. We consider spatiotemporal data represented in the form of events, each associated with location, type and occurrence time. The problem is to discover all significant sequential patterns denoting spatial and temporal relations between event types. In the paper, we adapted a microclustering approach and use it to effectively and efficiently discover sequential patterns and to reduce size of dataset of instances. Appropriate indexing structure has been proposed and notions already defined in the literature have been reformulated. We modify algorithms already defined in literature and propose an algorithm called Micro-ST-Miner for discovering sequential patterns in event-based spatiotemporal data.