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Dias, J. G. & Vermunt, J. K. (2008). A bootstrap-based aggregate classifier for model-based clustering. Computational Statistics. 23 (4), 643-659

J. M. Dias and V. J.K.,  "A bootstrap-based aggregate classifier for model-based clustering", in Computational Statistics, vol. 23, no. 4, pp. 643-659, 2008

@article{dias2008_1734885382536,
	author = "Dias, J. G. and Vermunt, J. K.",
	title = "A bootstrap-based aggregate classifier for model-based clustering",
	journal = "Computational Statistics",
	year = "2008",
	volume = "23",
	number = "4",
	doi = "10.1007/s00180-007-0103-7",
	pages = "643-659",
	url = "https://link.springer.com/article/10.1007%2Fs00180-007-0103-7"
}

TY  - JOUR
TI  - A bootstrap-based aggregate classifier for model-based clustering
T2  - Computational Statistics
VL  - 23
IS  - 4
AU  - Dias, J. G.
AU  - Vermunt, J. K.
PY  - 2008
SP  - 643-659
SN  - 0943-4062
DO  - 10.1007/s00180-007-0103-7
UR  - https://link.springer.com/article/10.1007%2Fs00180-007-0103-7
AB  - In model-based clustering, a situation in which true class labels are unknown and that is therefore also referred to as unsupervised learning, observations are typically classified by the Bayes modal rule. In this study, we assess whether alternative classifiers from the classification or supervised-learning literature—developed for situations in which class labels are known—can improve the Bayes rule. More specifically, we investigate the performance of bootstrap-based aggregate (bagging) rules after adapting these to the model-based clustering context. It is argued that specific issues, such as the label-switching problem, have to be carefully addressed when using bootstrap methods in model-based clustering. Our two Monte Carlo studies show that classification based on the Bayes rule is rather stable and difficult to improve by bootstrap-based aggregate rules, even for sparse data. An empirical example illustrates the various approaches described in this paper.
ER  -