Title
A Partial Join Approach for Mining Co-location Patterns: A Summary of Results
Abstract
Spatial co-location patterns represent the subsets of events whose instances are frequently located together in geographic space. We 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. We 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 Apriori algorithm as a building block and adopts the instance join method for residual instances not identified in transactions. We show that the algorithm is correct and complete in finding all co-location rules which have prevalence and conditional probability above the given thresholds. An experimental evaluation using synthetic datasets and a real dataset shows that our algorithm is computationally more efficient than the join-based algorithm.
Suggested Citation
Yoo, Jin Soung; Shekhar, Shashi.
(2005).
A Partial Join Approach for Mining Co-location Patterns: A Summary of Results.
Retrieved from the University of Minnesota Digital Conservancy,
https://hdl.handle.net/11299/215682.