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Discovering Colocation Patterns from Spatial Data Sets: A General Approach
December 2004 (vol. 16 no. 12)
pp. 1472-1485
Yan Huang, IEEE Computer Society
Shashi Shekhar, IEEE Computer Society
Hui Xiong, IEEE Computer Society
Given a collection of Boolean spatial features, the colocation pattern discovery process finds the subsets of features frequently located together. For example, the analysis of an ecology data set may reveal symbiotic species. The spatial colocation rule problem is different from the association rule problem since there is no natural notion of transactions in spatial data sets which are embedded in continuous geographic space. In this paper, we provide a transaction-free approach to mine colocation patterns by using the concept of proximity neighborhood. A new interest measure, a participation index, is also proposed for spatial colocation patterns. The participation index is used as the measure of prevalence of a colocation for two reasons. First, this measure is closely related to the {\rm{cross}}{\hbox{-}}K function, which is often used as a statistical measure of interaction among pairs of spatial features. Second, it also possesses an antimonotone property which can be exploited for computational efficiency. Furthermore, we design an algorithm to discover colocation patterns. This algorithm includes a novel multiresolution pruning technique. Finally, experimental results are provided to show the strength of the algorithm and design decisions related to performance tuning.

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Index Terms:
Colocation patterns, spatial association rules, participation index.
Citation:
Yan Huang, Shashi Shekhar, Hui Xiong, "Discovering Colocation Patterns from Spatial Data Sets: A General Approach," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 12, pp. 1472-1485, Dec. 2004, doi:10.1109/TKDE.2004.90
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