Abstract
We propose an assessing method of mixture model in a cluster analysis setting with integrated completed likelihood. For this purpose, the observed data are assigned to unknown clusters using a maximum a posteriori operator. Then, the integrated completed likelihood (ICL) is approximated using the Bayesian information criterion (BIC). Numerical experiments on simulated and real data of the resulting ICL criterion show that it performs well both for choosing a mixture model and a relevant number of clusters. In particular, ICL appears to be more robust than BIC to violation of some of the mixture model assumptions and it can select a number of dusters leading to a sensible partitioning of the data.
Keywords
Bayesian information criterionMixture modelCluster analysisMaximum a posteriori estimationMaximum likelihoodBayesian probabilityComputer scienceA priori and a posterioriArtificial intelligenceData modelingData miningPattern recognition (psychology)StatisticsMathematics
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Publication Info
- Year
- 2000
- Type
- article
- Volume
- 22
- Issue
- 7
- Pages
- 719-725
- Citations
- 1449
- Access
- Closed
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Cite This
Christophe Biernacki,
Gilles Celeux,
G. Govaert
(2000).
Assessing a mixture model for clustering with the integrated completed likelihood.
IEEE Transactions on Pattern Analysis and Machine Intelligence
, 22
(7)
, 719-725.
https://doi.org/10.1109/34.865189
Identifiers
- DOI
- 10.1109/34.865189