Estimating the number of clusters via a corrected clustering instability
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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 .
1990 ◽
Vol 29
(03)
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pp. 200-204
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2017 ◽
Vol 13
(2)
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pp. 1-12
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2008 ◽
Vol 2
(1)
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pp. 65-70
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2021 ◽
Vol 50
(2)
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pp. 187-209
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