BORDER: efficient computation of boundary points
IEEE Transactions on Knowledge and Data Engineering（2006）
摘要
This work addresses the problem of finding boundary points in multidimensional data sets. Boundary points are data points that are located at the margin of densely distributed data such as a cluster. We describe a novel approach called BORDER (a BOundaRy points DEtectoR) to detect such points. BORDER employs the state-of-the-art database technique - the Gorder kNN join and makes use of the special property of the reverse k nearest neighbor (RkNN). Experimental studies on data sets with varying characteristics indicate that BORDER is able to detect the boundary points effectively and efficiently.
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关键词
boundary point computation,database management systems,state-of-the-art database technique,knn join,reverse k nearest neighbor,multidimensional data sets,varying characteristic,boundary points,special property,database technique,efficient computation,boundary points detector,boundary point,reverse k-nearest neighbor.,multidimensional data set,border,k-nearest neighbor,data point,data mining,experimental study,novel approach,reverse k,databases,association rules,pattern analysis,information technology,multidimensional systems,detectors,k nearest neighbor,information analysis
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