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 which is the discove...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 which is the discovery of interesting rela-tionships and characteristics that may exist implicitly in spatial databases. So far, a lot of spatial clustering algorithms have been proposed in many applications such as pattern recognition, data analysis, and image processing and so forth. However most of the well-known clustering algorithms have some drawbacks which will be presented later when ap-plied in large spatial databases. To overcome these limitations, in this paper we propose a robust spatial clustering algorithm named NSCABDT (Novel Spatial Clustering Algorithm Based on Delaunay Triangulation). Delaunay dia-gram is used for determining neighborhoods based on the neighborhood notion, spatial association rules and colloca-tions being defined. NSCABDT demonstrates several important advantages over the previous works. Firstly, it even discovers arbitrary shape of cluster distribution. Secondly, in order to execute NSCABDT, we do not need to know any priori nature of distribution. Third, like DBSCAN, Experiments show that NSCABDT does not require so much CPU processing time. Finally it handles efficiently outliers.展开更多
文摘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 which is the discovery of interesting rela-tionships and characteristics that may exist implicitly in spatial databases. So far, a lot of spatial clustering algorithms have been proposed in many applications such as pattern recognition, data analysis, and image processing and so forth. However most of the well-known clustering algorithms have some drawbacks which will be presented later when ap-plied in large spatial databases. To overcome these limitations, in this paper we propose a robust spatial clustering algorithm named NSCABDT (Novel Spatial Clustering Algorithm Based on Delaunay Triangulation). Delaunay dia-gram is used for determining neighborhoods based on the neighborhood notion, spatial association rules and colloca-tions being defined. NSCABDT demonstrates several important advantages over the previous works. Firstly, it even discovers arbitrary shape of cluster distribution. Secondly, in order to execute NSCABDT, we do not need to know any priori nature of distribution. Third, like DBSCAN, Experiments show that NSCABDT does not require so much CPU processing time. Finally it handles efficiently outliers.
