ACM Computing Surveys (CSUR)
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
A polygon-based methodology for mining related spatial datasets
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics
Analyzing the composition of cities using spatial clustering
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
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The traditional point-based clustering algorithms when applied to geospatial polygons may produce clusters that are spatially disjoint due to their inability to consider various types of spatial relationships between polygons. In this paper, we propose to represent geospatial polygons as sets of spatial and non-spatial attributes. By representing a polygon as a set of spatial and non-spatial attributes we are able to take into account all the properties of a polygon (such as structural, topological and directional) that were ignored while using point-based representation of polygons, and that aid in the formation of high quality clusters. Based on this framework we propose a dissimilarity function that can be plugged into common state-of-the-art spatial clustering algorithms. The result is clusters of polygons that are more compact in terms of cluster validity and spatial contiguity. We show the effectiveness and robustness of our approach by applying our dissimilarity function on the traditional k-means clustering algorithm and testing it on a watershed dataset.