Dec. 18, 2006 to Dec. 22, 2006
Huiping Cao , The University of Hong Kong, Hong Kong
Nikos Mamoulis , The University of Hong Kong, Hong Kong
David W. Cheung , The University of Hong Kong, Hong Kong
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.59
Given a collection of trajectories of moving objects with different types (e.g., pumas, deers, vultures, etc.), we introduce the problem of discovering collocation episodes in them (e.g., if a puma is moving near a deer, then a vulture is also going to move close to the same deer with high probability within the next 3 minutes). Collocation episodes catch the inter-movement regularities among different types of objects. We formally define the problem of mining collocation episodes and propose two scaleable algorithms for its efficient solution. We empirically evaluate the performance of the proposed methods using synthetically generated data that emulate real-world object movements.
Huiping Cao, Nikos Mamoulis, David W. Cheung, "Discovery of Collocation Episodes in Spatiotemporal Data", ICDM, 2006, Sixth International Conference on Data Mining (ICDM'06), Sixth International Conference on Data Mining (ICDM'06) 2006, pp. 823-827, doi:10.1109/ICDM.2006.59