Abstract
We present a practical approach to nonparametric cluster analysis of large data sets. The number of clusters and the cluster centres are automatically derived by mode seeking with the mean shift procedure on a reduced set of points randomly selected from the data. The cluster boundaries are delineated using a k-nearest neighbour technique. The proposed algorithm is stable and efficient, a 10,000 point data set being decomposed in only a few seconds. Complex clustering examples and applications are discussed, and convergence of the gradient ascent mean shift procedure is demonstrated for arbitrary distribution and cardinality of the data.
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Received: 7 October 1998¶Accepted: 9 October 1998
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Comaniciu, D., Meer, P. Distribution Free Decomposition of Multivariate Data. Pattern Analysis & Applications 2, 22–30 (1999). https://doi.org/10.1007/s100440050011
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DOI: https://doi.org/10.1007/s100440050011