A Nonparametric Model for Event Discovery in the Geospatial-Temporal Space

The availability of geographical and temporal tagged documents enables many location and time based mining tasks. Event discovery is one of such tasks, which is to identify interesting happenings in the geographical and temporal space. In recent years, several techniques have been proposed. However, no existing work has provided a nonparametric algorithm for detecting events in the joint space crossing geographical and temporal dimensions. Furthermore, though some prior works proposed to capture the periodicities of topics in their solutions, some restrictions on the temporal patterns are often placed and they usually ignore the spatial patterns of the topics. To break through such limitations, in this paper we propose a novel nonparametric model to identify events in the geographical and temporal space, where any recurrent patterns of events can be automatically captured. In our approach, parameters are automatically determined by exploiting a Dirichlet Process. To reduce the influence from noisy terms in the detection, we distinguish its event role from its background role using a Bernoulli model in the solution. Experimental results on three real world datasets show the proposed algorithm outperforms previous state-of-the-art approaches.