Resampling approach for cluster model selection

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Date

2011-10-01

Author

Volkovich, Z.
Barzily, Z.
Weber, Gerhard Wilhelm
Toledano-Kitai, D.
Avros, R.

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In cluster analysis, selecting the number of clusters is an "ill-posed" problem of crucial importance. In this paper we propose a re-sampling method for assessing cluster stability. Our model suggests that samples' occurrences in clusters can be considered as realizations of the same random variable in the case of the "true" number of clusters. Thus, similarity between different cluster solutions is measured by means of compound and simple probability metrics. Compound criteria result in validation rules employing the stability content of clusters. Simple probability metrics, in particular those based on kernels, provide more flexible geometrical criteria. We analyze several applications of probability metrics combined with methods intended to simulate cluster occurrences. Numerical experiments are provided to demonstrate and compare the different metrics and simulation approaches.

Subject Keywords

Software, Artificial Intelligence

URI

https://hdl.handle.net/11511/57879

Journal

MACHINE LEARNING

DOI

https://doi.org/10.1007/s10994-011-5236-9

Collections

Graduate School of Applied Mathematics, Article

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Citation Formats

Z. Volkovich, Z. Barzily, G. W. Weber, D. Toledano-Kitai, and R. Avros, “Resampling approach for cluster model selection,” MACHINE LEARNING, pp. 209–248, 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57879.