Selecting an optimal clustering solution is a longstanding problem. In model-based clustering this amounts to choose the architecture of the model mixture distribution. Decisions to be made pertain to: cluster prototype distribution; number of mixture components; (optionally) restrictions on the clusters’ geometry. Classical pro- posals address this issue via penalized model selection criteria based on the observed likelihood function. In this study, we compare these techniques with the less explored cross-validation alternative, which is rather popular for many other data-driven opti- mized methods. We analyze both classical methods such as BIC, AIC, AIC3 and ICL, and several cross-validation schemes where the risk is defined in terms of minus the log-likelihood function. Selection methods are compared by using the Iris dataset.

Likelihood-type methods for comparing clustering solutions

Pietro Coretto
2019

Abstract

Selecting an optimal clustering solution is a longstanding problem. In model-based clustering this amounts to choose the architecture of the model mixture distribution. Decisions to be made pertain to: cluster prototype distribution; number of mixture components; (optionally) restrictions on the clusters’ geometry. Classical pro- posals address this issue via penalized model selection criteria based on the observed likelihood function. In this study, we compare these techniques with the less explored cross-validation alternative, which is rather popular for many other data-driven opti- mized methods. We analyze both classical methods such as BIC, AIC, AIC3 and ICL, and several cross-validation schemes where the risk is defined in terms of minus the log-likelihood function. Selection methods are compared by using the Iris dataset.
978-88-8317-108-6
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4734946
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