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Distribution free decomposition of multivariate data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (1998) 1451 602-610
DOI: 10.1007/bfb0033284
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Abstract

A practical approach to nonparametric cluster analysis of large data sets is presented. The number of clusters and the cluster centers are derived by applying the mean shift procedure on a reduced set of points randomly selected from the data. The cluster boundaries are delineated using a k-nearest neighbor technique. The resulting algorithm is stable and efficient, allowing the cluster decomposition of a 10000 point data set in only a few seconds. Complex clustering examples and applications are discussed.

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Comaniciu, D., & Meer, P. (1998). Distribution free decomposition of multivariate data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1451, pp. 602–610). Springer Verlag. https://doi.org/10.1007/bfb0033284

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