A Join-Less Approach for Co-Location Pattern Mining: A Summary of Results

Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. Co-location pattern discovery presents challenges since the instances of spatial features are embedded in a continuous space and share a variety of spatial relationships. A large fraction of the computation time is devoted to identifying the instances of co-location patterns. We propose a novel join-less approach for co-location pattern mining, which materializes spatial neighbor relationships with no loss of co-location instances and reduces the computational cost of identifying the instances. The joinless co-location mining algorithm is efficient since it uses an instance-lookup scheme instead of an expensive spatial or instance join operation for identifying co-location instances. We prove the join-less algorithm is correct and complete in finding co-location rules. The experimental evaluations using synthetic datasets and real world datasets show th...

• Type: Conference
• Year: 2005
• Where: ICDM
• Authors: Jin Soung Yoo, Shashi Shekhar, Mete Celik
• Keywords: Co-location Instances, Co-location Pattern, Data Mining, ICDM 2005, Spatial