Model-based Methods of Classification: Using the mclust Software in Chemometrics

By Chris Fraley, Adrian E. Raftery. 2007

311 citations recorded by the source corpus.

Due to recent advances in methods and software for model-based clustering, and to the interpretability of the results, clustering procedures based on probability models are increasingly preferred over heuristic methods. The clustering process estimates a model for the data that allows for overlapping clusters, producing a probabilistic clustering that quantifies the uncertainty of observations belonging to components of the mixture. The resulting clustering model can also be used for some other important problems in multivariate analysis, including density estimation and discriminant analysis. Examples of the use of model-based clustering and classification techniques in chemometric studies include multivariate image analysis, magnetic resonance imaging, microarray image segmentation, statistical process control, and food authenticity. We review model-based clustering and related methods for density estimation and discriminant analysis, and show how the R package mclust can be applied in each instance.

Subfields: Analytical Chemistry, Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6ee43bada03d9259d36801dfd1699e9440cf4fd8

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Citations in the corpus

MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering † (2007, background): “A couple of tutorials on mclust have also been published [14, 16].”
mclust Version 4 for R : Normal Mixture Modeling for Model-Based Clustering , Classification , and Density Estimation (2012, background): “A couple of tutorials on mclust have also been published [16, 18].”
mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models (2016, methodology): “It has been used in a broad range of contexts including geochemistry (Templ et al., 2008; Ellefsen et al., 2014), chemometrics (Fraley and Raftery, 2006a, 2007b), DNA sequence analysis (Verbist et al., 2015), gene expression data (Yeung et al., 2001; Li et al., 2005; Fraley and Raftery, 2006b),…”
Rapid, direct and non-destructive assessment of fossil organic matter via microRaman spectroscopy (2016, unknown): “means of multiple chemical processes [24, 28].”