The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Model-Based Gaussian and Non-Gaussian Clustering
Abstract
Abstract : The classification maximum likelihood approach is sufficiently general to encompass many current clustering algorithms, including those based on the sum of squares criterion and on the criterion of Friedman and Rubin (1967). However, as currently implemented, it does not allow the specification of which features (orientation, size and shape) are to be common to all clusters and which may differ between clusters. Also, it is restricted to Gaussian distributions and it does not allow for noise. We propose ways of overcoming these limitations. A reparameterization of the covariance matrix allows us to specify that some features, but not all, be the same for all clusters. A practical framework for non-Gaussian clustering is outlined, and a means of incorporating noise in the form of a Poisson process is described. An approximate Bayesian method for choosing the number of clusters is given. The performance of the proposed methods is studied by simulation, with encouraging results. The methods are applied to the analysis of a data set arising in the study of diabetes, and the results seem better than those of previous analyses. (RH)
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Cites
Testing a Point Null Hypothesis: The Irreconcilability of P Values and Evidence
Berger, J. O., & Sellke, T. (1987) · Journal of the American Statistical Association · doi:10.2307/2289131 · Cited by 548
Cited by
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558
Self-citationMcLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991
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
Monti, S., Tamayo, P., Mesirov, J., & Golub, T. (2003) · Machine Learning · doi:10.1023/a:1023949509487 · Cited by 2,116
Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.086 · Cited by 1,027
Metadata
- Authors
- Banfield, J. D., & Raftery, A. E.
- Venue
- Biometrics
- Publisher
- JSTOR
- Published
- 1993-09
- Volume
- 49
- Issue
- 3
- Pages
- 803
- ISSN
- 0006-341X (ISSN-L)
- OpenAlex
- W2082503527
- Cited by
- 2,395
- References
- 1
- FWCI
- 6.91
- Type
- journal article
- Language
- en
- Open access
- closed
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