The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Variable Selection for Model-Based Clustering
Abstract
We consider the problem of variable or feature selection for model-based clustering. The problem of comparing two nested subsets of variables is recast as a model comparison problem and addressed using approximate Bayes factors. A greedy search algorithm is proposed for finding a local optimum in model space. The resulting method selects variables (or features), the number of clusters, and the clustering model simultaneously. We applied the method to several simulated and real examples and found that removing irrelevant variables often improved performance. Compared with methods based on all of the variables, our variable selection method consistently yielded more accurate estimates of the number of groups and lower classification error rates, as well as more parsimonious clustering models and easier visualization of results.
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Cites
THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
FISHER, R. A. (1936) · Annals of Eugenics · doi:10.1111/j.1469-1809.1936.tb02137.x · Cited by 15,227
McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991
Pattern Recognition and Neural Networks
Ripley, B. D. (1996) · Cambridge University Press eBooks · doi:10.1017/cbo9780511812651 · Cited by 6,459
Lohrli, A. (1985) · Notes and Queries · doi:10.1093/notesj/32.3.377-a · Cited by 4,352
Model-Based Clustering, Discriminant Analysis, and Density Estimation
Fraley, C., & Raftery, A. E. (2002) · Journal of the American Statistical Association · doi:10.1198/016214502760047131 · Cited by 4,257
Self-citationTextures: A Photographic Album for Artists and Designers
Hersey, I., & Brodatz, P. (1968) · Leonardo · doi:10.2307/1571915 · Cited by 2,626
Model-Based Gaussian and Non-Gaussian Clustering
Banfield, J. D., & Raftery, A. E. (1993) · Biometrics · doi:10.2307/2532201 · Cited by 2,395
Self-citationComputing Bayes Factors by Combining Simulation and Asymptotic Approximations
Diciccio, T. J., Kass, R. E., Raftery, A., & Wasserman, L. (1997) · Journal of the American Statistical Association · doi:10.1080/01621459.1997.10474045 · Cited by 1,943
Self-citationSubset Selection in Regression
(2003) · Technometrics · doi:10.1198/tech.2003.s144 · Cited by 1,463
Unsupervised feature selection using feature similarity
Mitra, P., Murthy, C. A., & Pal, S. K. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.990133 · Cited by 1,460
Cited by
McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991
A framework for feature selection in clustering
Witten, D. M., & Tibshirani, R. (2010) · Journal of the American Statistical Association · doi:10.1198/jasa.2010.tm09415 · Cited by 696
High-dimensional data clustering
Bouveyron, C., Girard, S., & Schmid, C. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.02.009 · Cited by 382
Esmin, A. A. A., Coelho, R. A., & Matwin, S. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-013-9400-4 · Cited by 340
Finite mixture models and model-based clustering
Melnykov, V., & Maitra, R. (2010) · Statistics Surveys · doi:10.1214/09-ss053 · Cited by 314
Metadata
- Authors
- Raftery, A. E., & Dean, N.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2006-03
- Volume
- 101
- Issue
- 473
- Pages
- 168-178
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2047109555
- Cited by
- 521
- References
- 38
- FWCI
- 25.64
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
