Mixed-drove spatiotemporal co-occurrence pattern mining
Mixed-drove spatiotemporal co-occurrence patterns (MDCOPs) represent subsets of two or more different object-types whose instances are often located in spatial and temporal proximity. Discovering MDCOPs is an important problem with many applications such as identifying tactics in battlefields and games and tracking predator-prey interactions. However, mining MDCOPs is computationally very expensive because the interest measures are computationally complex, data sets are larger due to the archival history, and the set of candidate patterns is exponential in the number of object-types. We propose a monotonic composite interest measure for discovering MDCOPs and novel MDCOP mining algorithms. Analytical results show that the proposed algorithms are correct and complete. Experimental results also show that the proposed methods are computationally more efficient than naive alternatives.
mixed-drove spatiotemporal co-occurrence pattern spatiotemporal co-occurrence pattern mining composite interest measure spatiotemporal data mining