29 December 2008 A novel spatial clustering algorithm based on Delaunay triangulation
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Proceedings Volume 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA); 728530 (2008); doi: 10.1117/12.813354
Event: International Conference on Earth Observation Data Processing and Analysis, 2008, Wuhan, China
Abstract
Exploratory data analysis is increasingly more necessary as larger spatial data is managed in electro-magnetic media. Spatial clustering is one of the very important spatial data mining techniques. So far, a lot of spatial clustering algorithms have been proposed. In this paper we propose a robust spatial clustering algorithm named SCABDT (Spatial Clustering Algorithm Based on Delaunay Triangulation). SCABDT demonstrates important advantages over the previous works. First, it discovers even arbitrary shape of cluster distribution. Second, in order to execute SCABDT, we do not need to know any priori nature of distribution. Third, like DBSCAN, Experiments show that SCABDT does not require so much CPU processing time. Finally it handles efficiently outliers.
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Xiankun Yang, Weihong Cui, "A novel spatial clustering algorithm based on Delaunay triangulation", Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 728530 (29 December 2008); doi: 10.1117/12.813354; https://doi.org/10.1117/12.813354
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Data mining

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Data modeling

Geographic information systems

Medical imaging

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