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 monotonie 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.
|Original language||English (US)|
|Number of pages||14|
|Journal||IEEE Transactions on Knowledge and Data Engineering|
|State||Published - Oct 2008|
Bibliographical noteFunding Information:
The authors would like to thank the members of the Spatial Database and Spatial Data Mining Research Group at the University of Minnesota for their comments. They would also like to express their thanks to Kim Koffolt for improving the readability of this paper. This work was partially supported by the US Army Corps of Engineers under Contract W9132V-06-C-0011, the US National Science Foundation (NSF) Grant 0431141, the NSF Grant 0708604, the NSF Grant 0713214, and an NGA grant.
- Composite interest measure
- Mixed-drove spatiotemporal co-occurrence pattern
- Spatiotemporal co-occurrence pattern mining
- Spatiotemporal data mining