Partial Join Approach for Mining Co-Location Patterns: A Summary of Results

摘要

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.