A partial join approach for mining co-location patterns

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 partialjoin 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 al...

• Type: Conference
• Year: 2004
• Where: GIS
• Authors: Jin Soung Yoo, Shashi Shekhar
• Keywords: Co-location Miner Algorithm, Co-location Mining Algorithm, Database, GIS 2004, Spatial Co-location Patterns