Mining Statistically Significant Temporal Associations In Multiple Event Sequences

Abstract

We propose a two-phase method, called Multivariate Association Discovery (MAD), to mine temporal associations in multiple event sequences. It is assumed that a set of event sequences has been collected from an application, where each event has an id and an occurrence time. The goal is to detect temporal associations of events whose frequencies in the data are statistically significant. The motivation of our work is the observation that in practice many associated events in multiple temporal sequences do not occur concurrently but sequentially. In an empirical study, we apply MAD to tackle two problems originating from different application domains. The experimental results show that our method performed better than other related methods in these domains.