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Mixed-Drove spatio-temporal 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, games, and predator-prey interactions. However, mining MDCOPs is computationally very expensive because the interest measures are computationally complex, datasets are larger due to the archival history, and the set of candidate patterns is exponential in the number of object-types. The authors propose a monotonic composite interest measure for discovering MDCOPs and novel MDCOP mining algorithms.
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