Please use this identifier to cite or link to this item: https://doi.org/10.1109/TKDE.2006.38
Title: BORDER: Efficient computation of boundary points
Authors: Xia, C.
Hsu, W. 
Lee, M.L. 
Ooi, B.C. 
Keywords: Boundary points
k-nearest neighbor
kNN join
Reverse k-nearest neighbor
Issue Date: 2006
Citation: Xia, C., Hsu, W., Lee, M.L., Ooi, B.C. (2006). BORDER: Efficient computation of boundary points. IEEE Transactions on Knowledge and Data Engineering 18 (3) : 289-303. ScholarBank@NUS Repository. https://doi.org/10.1109/TKDE.2006.38
Abstract: 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. © 2006 IEEE.
Source Title: IEEE Transactions on Knowledge and Data Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/39278
ISSN: 10414347
DOI: 10.1109/TKDE.2006.38
Appears in Collections:Staff Publications

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