A Survey on Spatiotemporal Co-occurrence Pattern Mining Techniques

Abstract

Spatiotemporal data mining (STDM) is the methodology for determining meaningful patterns from the information rich spatiotemporal data. The phenomenal growth of spatiotemporal data along with the emergence of IOT, smart devices, social media, and location-based services, etc., has accentuated the demand for efficient discovery of spatiotemporal knowledge. STDM has a widespread use in several real-life situations. STDM techniques are generally classified based on the spatiotemporal output patterns family types like spatiotemporal outlier detection, spatiotemporal associations, etc. Spatiotemporal association pattern is also referred as spatiotemporal frequent pattern mining or spatiotemporal co-occurrence pattern (STCOP) mining. Traditional transaction dataset, based approaches are not useful when it comes to obtaining knowledge or patterns from spatiotemporal datasets. This survey gives an overview of the recent spatiotemporal data mining techniques available, giving special focus to spatiotemporal co-occurrence pattern mining family.