Abstract In order to detect boundary points of clusters effectively, an algorithm is proposed, namely boundary points detecting algorithm based on coefficient of variation(BAND). BAND computes the average distance between one object and its k-distance neighbors. The density of each object is obtained by the reciprocal of average distance. Then the boundary points are found by using the coefficient of variation to portray the distribution of data objects. The experimental results show BAND effectively detects boundary points on noisy datasets with clusters of arbitrary shapes, sizes and different densities.
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2009, Vol. 22 
