@article {Atkinson2007Exploratory,
	title = {Exploratory tools for clustering multivariate data},
	journal = {Computational Statistics \& Data Analysis},
	volume = {52},
	number = {1},
	year = {2007},
	month = {September},
	pages = {272{\textendash}285},
	abstract = {The forward search provides a series of robust parameter estimates based on increasing numbers of observations. The resulting series of robust Mahalanobis distances is used to cluster multivariate normal data. The method depends on envelopes of the distribution of the test statistics in forward plots. These envelopes can be found by simulation; flexible polynomial approximations to the envelopes are given. New graphical tools provide methods not only of detecting clusters but also of determining their membership. Comparisons are made with mclust and k -means clustering.},
	keywords = {algorithms, cluster, data-exploration, it},
	issn = {01679473},
	doi = {10.1016/j.csda.2006.12.034},
	url = {http://dx.doi.org/10.1016/j.csda.2006.12.034},
	author = {Atkinson, A. and Riani, M.}
}
@article {Fraley2007Bayesian,
	title = {Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering},
	journal = {Journal of Classification},
	volume = {24},
	number = {2},
	year = {2007},
	month = {September},
	pages = {155{\textendash}181},
	abstract = {Abstract\&nbsp;\&nbsp;Normal mixture models are widely used for statistical modeling of data, including cluster analysis. However maximum likelihood estimation (MLE) for normal mixtures using the EM algorithm may fail as the result of singularities or degeneracies. To avoid this, we propose replacing the MLE by a maximum a posteriori (MAP) estimator, also found by the EM algorithm. For choosing the number of components and the model parameterization, we propose a modified version of BIC, where the likelihood is evaluated at the MAP instead of the MLE. We use a highly dispersed proper conjugate prior, containing a small fraction of one observation{\textquoteright}s worth of information. The resulting method avoids degeneracies and singularities, but when these are not present it gives similar results to the standard method using MLE, EM and BIC.},
	keywords = {classification, cluster, models, taxonomy},
	issn = {0176-4268},
	doi = {10.1007/s00357-007-0004-5},
	url = {http://dx.doi.org/10.1007/s00357-007-0004-5},
	author = {Fraley, Chris and Raftery, Adrian E.}
}
