Abstract
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 (kNNG) 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.
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Qian, F., Chiew, K., He, Q., Huang, H., Ma, L. (2013). Discovery of Regional Co-location Patterns with k-Nearest Neighbor Graph. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37453-1_15
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DOI: https://doi.org/10.1007/978-3-642-37453-1_15
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