Mining Co-Location Patterns with Rare Events from Spatial Data Sets |
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Authors: | Yan Huang Jian Pei Hui Xiong |
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Affiliation: | (1) Department of Computer Science and Engineering, University of North Texas, Texas, USA;(2) School of Computing Science, Simon Fraser University, Burnaby, Canada;(3) Management Science and Information Systems Department, Rutgers University, Newark, USA |
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Abstract: | A co-location pattern is a group of spatial features/events that are frequently co-located in the same region. For example, human cases of West
Nile Virus often occur in regions with poor mosquito control and the presence of birds. For co-location pattern mining, previous
studies often emphasize the equal participation of every spatial feature. As a result, interesting patterns involving events
with substantially different frequency cannot be captured. In this paper, we address the problem of mining co-location patterns with rare spatial features. Specifically, we first propose a new measure called the maximal participation ratio (maxPR) and show that a co-location pattern with a relatively high maxPR value corresponds to a co-location pattern containing
rare spatial events. Furthermore, we identify a weak monotonicity property of the maxPR measure. This property can help to
develop an efficient algorithm to mine patterns with high maxPR values. As demonstrated by our experiments, our approach is
effective in identifying co-location patterns with rare events, and is efficient and scalable for large-scale data sets.
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Keywords: | Spatial data mining Co-location patterns Spatial association rules |
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