Estimating the number of clusters in a data set via the gap statistic
Robert Tibshirani Department of Health Research and Policy and Department of Statistics, Stanford University, Stanford, CA 94305, USA. E‐mail: tibs@stat.stanford.edu
Abstract
We propose a method (the ‘gap statistic’) for estimating the number of clusters (groups) in a set of data. The technique uses the output of any clustering algorithm (e.g. K‐means or hierarchical), comparing the change in within‐cluster dispersion with that expected under an appropriate reference null distribution. Some theory is developed for the proposal and a simulation study shows that the gap statistic usually outperforms other methods that have been proposed in the literature.
Citing Literature
Number of times cited according to CrossRef: 2078
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