Variable Selection for Model-Based Clustering

By Adrian E. Raftery, Nema Dean. 2006

563 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d8f99ea987612bc8be0742031dc766dabd6cec70

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In Artificial Intelligence

Citations in the corpus

A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data (2013, methodology): “1998; Chan and Hall 2010; Raftery and Dean 2006), model-based methods such as (Pan and Shen 2007; Wang ans Zhu 2008), and criterion-based methods such as (Witten and Tibshirani 2010; Raftery and Dean 2006).”
Regularized k-means clustering of high-dimensional data and its asymptotic consistency (2012, background): “Although variable selection for regression has been extensively studied, analogous result for clustering is limited, such as [19, 16, 26, 24, 12, 10].”
Model-Based Clustering (2016, methodology): “Raftery and Dean (2006) propose a variable selection method that utilizes a greedy search of the model space.”
Penalized Model-Based Clustering with Application to Variable Selection (2007, methodology): “…variable selection in regression, is to sequentially compare two nested models to determine whether an attribute should be included in or excluded from the current model based on a greedy search (Raftery and Dean, 2006), which may be computationally too time-consuming for high-dimensional data.”
Extending the Akaike Information Criterion to Mixture Regression Models (2007, background): “Recently, Raftery and Dean (2006) investigated the simultaneous selection of the variables to use for clustering and the number of clusters retained in the model; however, their model does not specify a family of multiple regression models to predict the response variable across components.”
Variable Selection for Clustering with Gaussian Mixture Models (2009, methodology): “Raftery and Dean (2006) recast the problem of comparing two nested variable subsets as a model comparison problem and address it using Bayes’ factor.”
Selection of Variables for Cluster Analysis and Classification Rules (2006, methodology): “the method proposed by Raftery and Dean (2006) performs selection of variables and estimation of the number of clusters simultaneously, the results are reported separately in Table 2.”
A Framework for Feature Selection in Clustering (2010, methodology): “Raftery and Dean (2006) also present a method for feature selection in the model-based clustering setting, using an entirely different approach.”
Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering (2007, background): “Raftery and Dean (2005) developed a model-based clustering that incorporates variable selection as an integral part of the procedure.”
Model-based clustering of high-dimensional data: A review (2014, methodology): “Variable selection and clustering in a GMM context The clustvarsel package for the R software implements the approach proposed by [83].”
Hypothesis test for normal mixture models: The EM approach (2009, methodology): “et for male and female schizophrenia patients [Everitt (1996)], and used in hematology studies [McLaren (1996)]. They play a fundamental role in cluster analysis [Tadesse, Sha and Vannucci (2005) and Raftery and Dean (2006)], and in the study of the false discovery rate [Efron (2004), McLachlan, Bea”
Model-based Methods of Classification: Using the mclust Software in Chemometrics (2007, background): “…recent work on extension to large data sets (Wehrens et al. 2004; Fraley et al. 2005), to high-dimensional data (McLachlan, Peel, and Bean 2003; Raftery and Dean 2006), and to models involving categorical data (Handcock, Raftery, and Tantrum 2005; Handcock, Tantrum, Shortreed, and Hoff 2006).”