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Abstract:
Spatial co-location patterns represent the subsets of events whose instances are frequently located together in geographic space. The authors identified the computational bottleneck in the execution time of a current co-location mining algorithm. A large fraction of the join-based co-location miner algorithm is devoted to computing joins to identify instances of candidate co-location patterns. They propose a novel partial-join approach for mining co-location patterns efficiently. It transactionizes continuous spatial data while keeping track of the spatial information not modeled by transactions. It uses a transaction-based "a priori" algorithm as a building block and adopts the instance join method for residual instances not identified in transactions. The authors show that the algorithm is correct and complete in finding all co-location rules that have prevalence and conditional probability above the given thresholds. An experimental evaluation using synthetic data sets and a real data set shows that their algorithm is computationally more efficient than the join-based algorithm.
| Limitations: |
 APPROVED FOR PUBLIC RELEASE |
| Description: |
Technical rept. |
| Pages: |
12 |
| Report Date: |
29 DEC 2005 |
| Contract Number: |
DAAD190120014, NASANCC21231 |
| Report Number: |
A214444 |
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Keywords relating to this report:
