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GIS
2008
ACM

Density based co-location pattern discovery

14 years 11 months ago
Density based co-location pattern discovery
Co-location pattern discovery is to find classes of spatial objects that are frequently located together. For example, if two categories of businesses often locate together, they might be identified as a co-location pattern; if several biologic species frequently live in nearby places, they might be a co-location pattern. Most existing co-location pattern discovery methods are generate-and-test methods, that is, generate candidates, and test each candidate to determine whether it is a co-location pattern. In the test step, we identify instances of a candidate to obtain its prevalence. In general, instance identification is very costly. In order to reduce the computational cost of identifying instances, we propose a density based approach. We divide objects into partitions and identifying instances in dense partitions first. A dynamic upper bound of the prevalence for a candidate is maintained. If the current upper bound becomes less than a threshold, we stop identifying its instances ...
Xiangye Xiao, Xing Xie, Qiong Luo, Wei-Ying Ma
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2008
Where GIS
Authors Xiangye Xiao, Xing Xie, Qiong Luo, Wei-Ying Ma
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