A partial join approach for mining co-location patterns

  • Authors:

  • Jin Soung Yoo;Shashi Shekhar;John Smith;Julius P. Kumquat

  • Affiliations:

  • University of Minnesota, Minneapolis, MN;University of Minnesota, Minneapolis, MN;The Kumquat Consortium;The Kumquat Consortium

  • Venue:
  • Proceedings of the 12th annual ACM international workshop on Geographic information systems
  • Year:
  • 2004

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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.