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Journal Article

Clustering stability: an overview

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von Luxburg,  U
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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von Luxburg, U. (2010). Clustering stability: an overview. Foundations and Trends in Machine Learning, 2(3), 235-274. doi:10.1561/2200000008.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-BF1C-0
Abstract
A popular method for selecting the number of clusters is based on stability arguments: one chooses the number of clusters such that the corresponding clustering results are "most stable". In recent years, a series of papers has analyzed the behavior of this method from a theoretical point of view. However, the results are very technical and difficult to interpret for non-experts. In this paper we give a high-level overview about the existing literature on clustering stability. In addition to presenting the results in a slightly informal but accessible way, we relate them to each other and discuss their different implications.