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Mixed-Drove Spatiotemporal Co-Occurrence Pattern Mining
October 2008 (vol. 20 no. 10)
pp. 1322-1335
Mete Celik, University of Minnesota, Minneapolis
Shashi Shekhar, University of Minnesota, Minneapolis
James P. Rogers, U.S. Army ERDC, Alexandria
James A. Shine, U.S. Army ERDC, Alexandria
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. 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.

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Index Terms:
Mining methods and algorithms, Data mining, Spatial databases and GIS
Mete Celik, Shashi Shekhar, James P. Rogers, James A. Shine, "Mixed-Drove Spatiotemporal Co-Occurrence Pattern Mining," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 10, pp. 1322-1335, Oct. 2008, doi:10.1109/TKDE.2008.97
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