Resampling Method for Unsupervised Estimation of Cluster Validity
Neural Computation
A sober look at clustering stability
COLT'06 Proceedings of the 19th annual conference on Learning Theory
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Proceedings of the 6th European Lisp Workshop
Clustering Stability: An Overview
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Center-based clustering under perturbation stability
Information Processing Letters
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Stratified k-means clustering over a deep web data source
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Maximum volume clustering: a new discriminative clustering approach
The Journal of Machine Learning Research
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We consider the stability of k-means clustering problems. Clustering stability is a common heuristics used to determine the number of clusters in a wide variety of clustering applications. We continue the theoretical analysis of clustering stability by establishing a complete characterization of clustering stability in terms of the number of optimal solutions to the clustering optimization problem. Our results complement earlier work of Ben-David, von Luxburg and Pál, by settling the main problem left open there. Our analysis shows that, for probability distributions with finite support, the stability of k-means clusterings depends solely on the number of optimal solutions to the underlying optimization problem for the data distribution. These results challenge the common belief and practice that view stability as an indicator of the validity, or meaningfulness, of the choice of a clustering algorithm and number of clusters.