A spatial co-location is a set of spatial events being frequently observed together in nearby geographic space. A common framework for mining spatial association patterns employs a level-wised search method (like Apriori). However, the Apriori-based algorithms do not scale well for discovering long co-location patterns in large or dense spatial neighborhoods and can be restricted for only short pattern discovery. To address this problem, we propose an algorithm for finding maximal co-located event sets which concisely represent all co-location patterns. The proposed algorithm generates only most promising candidates, traverses the pattern search space in depth-first manner with an effective pruning scheme, and reduces expensive co-location instance search operations. Our experiment result shows that the proposed algorithm is computationally effective when mining maximal co-locations. © 2011 Springer-Verlag.
CITATION STYLE
Yoo, J. S., & Bow, M. (2011). Mining maximal co-located event sets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6634 LNAI, pp. 351–362). Springer Verlag. https://doi.org/10.1007/978-3-642-20841-6_29
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