A join-less approach for co-location pattern mining: A summary of results

Jin Soung Yoo, Shashi Shekhar, Mete Celik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

63 Scopus citations

Abstract

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. The experimental evaluations show the join-less algorithm performs more efficiently than a current join-based algorithm and is scalable in dense spatial datasets.

Original languageEnglish (US)
Title of host publicationProceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005
Pages813-816
Number of pages4
DOIs
StatePublished - 2005
Event5th IEEE International Conference on Data Mining, ICDM 2005 - Houston, TX, United States
Duration: Nov 27 2005Nov 30 2005

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other5th IEEE International Conference on Data Mining, ICDM 2005
CountryUnited States
CityHouston, TX
Period11/27/0511/30/05

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