Title :
BORDER: efficient computation of boundary points
Author :
Xia, Chenyi ; Hsu, Wynne ; Lee, Mong Li ; Ooi, Beng Chin
Author_Institution :
Sch. of Comput., Nat. Univ. of Singapore, Singapore
fDate :
3/1/2006 12:00:00 AM
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.
Keywords :
data mining; database management systems; BORDER; BOundaRy points DEtectoR; boundary point computation; database technique; multidimensional data sets; reverse k nearest neighbor; Association rules; Data mining; Databases; Detectors; Diseases; Information analysis; Information technology; Multidimensional systems; Nearest neighbor searches; Pattern analysis; Boundary points; k-nearest neighbor; kNN join; reverse k-nearest neighbor.;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
DOI :
10.1109/TKDE.2006.38