Discovering partial spatio-temporal co-occurrence patterns

Spatio-temporal co-occurrence patterns represent subsets of object-types that are often located together in space and time. The aim of the discovery of partial spatio-temporal cooccurrence patterns (PACOPs) is to find co-occurrences of the object-types that are partially present in the database. Discovering PACOPs is an important problem with many applications such as discovering interactions between animals and identifying tactics in battlefields and games. However, mining PACOPs is computationally very expensive because the interest measures are computationally complex, databases are larger due to the archival history, and the set of candidate patterns is exponential in the number of object-types. Previous studies on discovering spatio-temporal co-occurrence patterns do not take into account the presence period (lifetime) of the objects in the database. In this paper, we define the problem of mining PACOPs, propose a new monotonic composite interest measure, and propose a novel PACOP mining algorithm. The experimental results show that the proposed algorithm is computationally more efficient than naïve alternatives.