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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Discovery of Regional Co-location Patterns with k-Nearest Neighbor Graph
verfasst von : Feng Qian, Kevin Chiew, Qinming He, Hao Huang, Lianhang Ma
Erschienen in: Advances in Knowledge Discovery and Data Mining
Verlag: Springer Berlin Heidelberg
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The spatial co-location pattern mining discovers the subsets of features of which the events are frequently located together in a geographic space. The current research on this topic adopts a distance threshold that has limitations in spatial data sets with various magnitudes of neighborhood distances, especially for mining of regional co-location patterns. In this paper, we propose a hierarchical co-location mining framework by considering both varieties of neighborhood distances and spatial heterogeneity. By adopting
k
-nearest neighbor graph (
k
NNG) instead of distance threshold, we propose “distance variation coefficient” as a new measure to drive the mining process and determine an individual neighborhood relationship graph for each region. The experimental results on a real world data set verify the effectiveness of our framework.