Gaussian Mixture Model-Based Clustering of Multivariate Data Using Soft Computing Hybrid Algorithm
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-79357-9_49
Reference15 articles.
1. McLachlan, G.J., Rathnayake, S.: On the number of components in a Gaussian mixture model. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 4(5), 341–355 (2014)
2. Bozdogan, H.: Choosing the number of clusters, subset selection of variables, and outlier detection in the standard mixture model cluster analysis. In: Diday, E., et al. (eds.) Invited paper in New Approaches in Classification and Data Analysis, pp. 169–177. Springer, New York (1994). https://doi.org/10.1007/978-3-642-51175-2_19
3. Scrucca, L., Raftery, A.E.: clustvarsel: a package implementing variable selection for Gaussian model-based clustering in R. J. Stat. Softw. 84 (2018)
4. McNicholas, P.D.: Model-based clustering. J. Classif. 33(3), 331–373 (2016)
5. Celeux, G., Frühwirth-Schnatter, S., Robert, C.P.: Model selection for mixture models perspectives and strategies. In: Handbook of Mixture Analysis, pp. 121–160 (2018)
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