The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
On the number of components in a Gaussian mixture model
Abstract
Mixture distributions, in particular normal mixtures, are applied to data with two main purposes in mind. One is to provide an appealing semiparametric framework in which to model unknown distributional shapes, as an alternative to, say, the kernel density method. The other is to use the mixture model to provide a probabilistic clustering of the data into g clusters corresponding to the g components in the mixture model. In both situations, there is the question of how many components to include in the normal mixture model. We review various methods that have been proposed to answer this question.WIREs Data Mining Knowl Discov 2014, 4:341–355. doi: 10.1002/widm.1135 This article is categorized under:Technologies > Machine Learning
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Citations
Cites
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558
McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991
Self-citationThe Jackknife, the Bootstrap and Other Resampling Plans
Efron, B. (1982) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970319 · Cited by 8,120
Finding Groups in Data: An Introduction to Cluster Analysis
Kaufman, L., & Rousseeuw, P. J. (1990) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316801 · Cited by 7,344
Estimating the Number of Clusters in a Data Set Via the Gap Statistic
Tibshirani, R., Walther, G., & Hastie, T. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00293 · Cited by 6,205
Testing the number of components in a normal mixture
Lo, Y. (2001) · Biometrika · doi:10.1093/biomet/88.3.767 · Cited by 4,640
An entropy criterion for assessing the number of clusters in a mixture model
Celeux, G., & Soromenho, G. (1996) · Journal of Classification · doi:10.1007/bf01246098 · Cited by 2,448 · cited 3×
“Celeux and Soromenho 63 proposed using the estimated entropy EN ( ̂𝜏 (after normalization) as a criterion in its own right for choosing the number of clusters.”
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Cited by
McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991
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Metadata
- Authors
- McLachlan, G. J., & Rathnayake, S.
- Venue
- WIREs Data Mining and Knowledge Discovery
- Publisher
- Wiley
- Published
- 2014-09
- Volume
- 4
- Issue
- 5
- Pages
- 341-355
- ISSN
- 1942-4787 (ISSN-L)
- 1942-4795
- OpenAlex
- W1952261593
- Cited by
- 249
- References
- 7
- FWCI
- 5.61
- Type
- journal article
- Language
- en
- Open access
- closed
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