Mixed-Drove Spatio-Temporal Co-occurence Pattern Mining: A Summary of Results

Authors:
Mete Celik;Shashi Shekhar;James P. Rogers;James A. Shine;Jin Soung Yoo
Affiliations:
University of Minnesota, USA;University of Minnesota, USA;U.S. Army ERDC, USA;U.S. Army ERDC, USA;University of Minnesota, USA
Venue:
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Year:
2006

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Abstract

Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of object-types that are located together in space and time. 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. We propose a monotonic composite interest measure for discovering MDCOPs and a novel MDCOP mining algorithm. Analytical and experimental results show that the proposed algorithm is correct and complete. Results also show the proposed method is computationally more efficient than naïve alternatives.