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Issue No.03 - March (2006 vol.18)
pp: 289-303
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.
Boundary points, kNN join, k-nearest neighbor, reverse k-nearest neighbor.
Chenyi Xia, Wynne Hsu, Mong Li Lee, Beng Chin Ooi, "BORDER: Efficient Computation of Boundary Points", IEEE Transactions on Knowledge & Data Engineering, vol.18, no. 3, pp. 289-303, March 2006, doi:10.1109/TKDE.2006.38
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