| 摘要 |
Described is using density to efficiently mine co-location patterns, such as closely located businesses frequently found together in business listing databases, geographic search logs, and/or GPS-based data. A data space of such information is geographically partitioned into a grid of cells, with dense cells scanned first. A dynamic upper bound of prevalence measure of co-location patterns is maintained during the scanning process. If the current upper bound is smaller than a threshold, the scanning is stopped, thereby significantly reducing the computation cost for processing many cells, while providing suitable results.
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