A partial join approach for mining co-location patterns

Jin Soung Yoo, Shashi Shekhar

Research output: Contribution to conferencePaperpeer-review

134 Scopus citations

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.

Original languageEnglish (US)
Pages241-249
Number of pages9
StatePublished - Dec 1 2004
EventGIS 2004: Proceedings of the Twelfth ACM International Symposium on Advances in Geographic Information Systems - Washington, DC, United States
Duration: Nov 12 2004Nov 13 2004

Other

OtherGIS 2004: Proceedings of the Twelfth ACM International Symposium on Advances in Geographic Information Systems
Country/TerritoryUnited States
CityWashington, DC
Period11/12/0411/13/04

Keywords

  • Association rule
  • Co-location
  • Join
  • Spatial data mining

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