Estimating the number of clusters via a corrected clustering instability

Jonas M. B. Haslbeck () and Dirk U. Wulff ()
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Jonas M. B. Haslbeck: University of Amsterdam
Dirk U. Wulff: University of Basel

Computational Statistics, 2020, vol. 35, issue 4, No 16, 1879-1894

Abstract: Abstract We improve instability-based methods for the selection of the number of clusters k in cluster analysis by developing a corrected clustering distance that corrects for the unwanted influence of the distribution of cluster sizes on cluster instability. We show that our corrected instability measure outperforms current instability-based measures across the whole sequence of possible k, overcoming limitations of current insability-based methods for large k. We also compare, for the first time, model-based and model-free approaches to determining cluster-instability and find their performance to be comparable. We make our method available in the R-package cstab.

Keywords: Cluster analysis; k-means; Stability; Resampling (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00180-020-00981-5

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