Enhanced Model-Based Clustering, Density Estimation, and Discriminant Analysis Software: MCLUST

By Chris Fraley, Adrian E. Raftery. Journal of Classification, 2003

325 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6fdb320b1ec5c9a61ee95905d68e93d4c18d8638

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In Artificial Intelligence

Citations in the corpus

Variable Selection for Model-Based Clustering (2006, methodology): “A list of the models available in the mclust software (Fraley and Raftery 2003), which allows this type of eigenvalue decomposition Gaussian clustering, is given in Table 1.”
Parsimonious Gaussian mixture models (2008, methodology): “These models are based on assuming a latent Gaussian model structure for each population; the latent Gaussian model is closely related to the mixture of factor analyzers model (Ghahramani and Hinton 1997).”
Low‐frequency nonlinearity and regime behavior in the Northern Hemisphere extratropical atmosphere (2017, methodology): “New nonstationary analysis methods or model-based clustering methods [Fraley and Raftery, 1998, 2003] are needed.”
Germline and Somatic Mutations in Homologous Recombination Genes Predict Platinum Response and Survival in Ovarian, Fallopian Tube, and Peritoneal Carcinomas (2013, unknown): “read depth approach, but incorporating normal mixture modeling via expectation maximization (34), which allowed the detection of copy number state changes that were present in a proportion of the complex population of cells in the tumor sample.”
Heteroscedastic factor mixture analysis (2010, background): “…quantitative data, the distributional form of the density of the Statistical Modelling 2010; 10(4): 441–460 observations in each of the underlying populations is usually modelled as multivariate Gaussian and the goal is to decompose the data into its mixture components (Fraley and Raftery, 2003).”
Variable Selection for Clustering with Gaussian Mixture Models (2009, methodology): “Those 28 models are available in the mixmod software (Biernacki et al., 2006) and, for most of them, in the mclust software (Fraley and Raftery, 2003).”
Model-based Methods of Classification: Using the mclust Software in Chemometrics (2007, methodology): “…we illustrate model-based clustering, density estimation and discriminant analysis using the R (R Development Core Team 2006) package mclust (Fraley and Raftery 1999, 2003, 2006a) available as a contributed package from the Comprehensive R Archive Network (CRAN) at…”
Using unlabelled data to update classification rules with applications in food authenticity studies (2006, methodology): “Call these estimates π̂.0/ and θ̂.0/ (these can be found by using mclust; Fraley and Raftery (1999, 2003)).”
Choosing Outcomes versus Choosing Products: Consumer‐Focused Retirement Investment Advice (2008, methodology): “An optimal model-based clustering routine that uses the Bayesian Information Criterion to choose among models (Fraley and Raftery 2003) was applied to both retirement distributions built in the second year.”
Model‐based clustering for social networks (2007, background): “context in the R package mclust ( Fraley and Raftery 1998, 2002, 2003 ).”
Population structure of the oldest known macroscopic communities from Mistaken Point, Newfoundland (2013, background): “…denoted ‘‘E’’ (constrained equal) and ‘‘V’’ (unconstrained) so that each model can be assigned a three-letter code that corresponds to its specific parameters: EEE (equal variance), EEV (equal volume and shape), VEV (equal shape), and VVV (unconstrained) (see Fraley and Raftery 1999, 2003, 2007).”
Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering (2007, methodology): “Many of these models can be estimated by the MCLUST software (Fraley and Raftery 1999, 2003), and there they are designated by a three-letter symbol, where the three letters indicate volume, shape and orientation, respectively.”
mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models (2016, methodology): “Earlier versions of the package have been described in Fraley and Raftery (1999), Fraley and Raftery (2003), and Fraley et al. (2012).”