2013 IEEE 13th International Conference on Data Mining (2006)
Dec. 18, 2006 to Dec. 22, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.59
Nikos Mamoulis , The University of Hong Kong, Hong Kong
David W. Cheung , The University of Hong Kong, Hong Kong
Huiping Cao , The University of Hong Kong, Hong Kong
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.
Nikos Mamoulis, David W. Cheung, Huiping Cao, "Discovery of Collocation Episodes in Spatiotemporal Data", 2013 IEEE 13th International Conference on Data Mining, vol. 00, no. , pp. 823-827, 2006, doi:10.1109/ICDM.2006.59