Add to ORKGFinite mixture models are being used increasingly to model a wide variety of random phenomena for clustering, classification and density estimation. mclust is a powerful and popular package which allows modelling of data as a Gaussian finite mixture with different covariance structures and different numbers of mixture components, for a variety of purposes of analysis. Recently, version 5 of the package has been made available on CRAN. This updated version adds new covariance structures, dimension reduction capabilities for visualisation, model selection criteria, initialisation strategies for the EM algorithm, and bootstrap-based inference, making it a full-featured R package for data analysis via finite mixture modelling.Science Foundation...
The use of a finite mixture of normal distributions in model-based clustering allows to capture non...
The following mixture model-based clustering methods are compared in a simulation study with one-dim...
Data is ever increasing with today’s many technological advances in terms of both quantity and dimen...
Finite mixture modeling provides a framework for cluster analysis based on parsimonious Gaussian mix...
In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributi...
Due to recent advances in methods and software for model-based clustering, and to the interpretabili...
Finite mixture models have a long history in statistics, having been used to model population hetero...
Finite mixture models are being commonly used in a wide range of applications in practice concerning...
The important role of finite mixture models in the statistical analysis of data is underscored by th...
Finite mixtures of von Mises-Fisher distributions allow to apply model-based clustering methods to d...
Finite mixtures of von Mises-Fisher distributions allow to apply model-based clustering methods to d...
In model-based clustering mixture models are used to group data points into clusters. A useful conce...
Finite mixture models are a popular method for modelling unobserved heterogeneity or for approximati...
A new method is proposed to generate sample Gaussian mixture distributions according to prespecified...
Finite mixture models are finite-dimensional generalizations of probabilistic models, which express ...
The use of a finite mixture of normal distributions in model-based clustering allows to capture non...
The following mixture model-based clustering methods are compared in a simulation study with one-dim...
Data is ever increasing with today’s many technological advances in terms of both quantity and dimen...
Finite mixture modeling provides a framework for cluster analysis based on parsimonious Gaussian mix...
In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributi...
Due to recent advances in methods and software for model-based clustering, and to the interpretabili...
Finite mixture models have a long history in statistics, having been used to model population hetero...
Finite mixture models are being commonly used in a wide range of applications in practice concerning...
The important role of finite mixture models in the statistical analysis of data is underscored by th...
Finite mixtures of von Mises-Fisher distributions allow to apply model-based clustering methods to d...
Finite mixtures of von Mises-Fisher distributions allow to apply model-based clustering methods to d...
In model-based clustering mixture models are used to group data points into clusters. A useful conce...
Finite mixture models are a popular method for modelling unobserved heterogeneity or for approximati...
A new method is proposed to generate sample Gaussian mixture distributions according to prespecified...
Finite mixture models are finite-dimensional generalizations of probabilistic models, which express ...
The use of a finite mixture of normal distributions in model-based clustering allows to capture non...
The following mixture model-based clustering methods are compared in a simulation study with one-dim...
Data is ever increasing with today’s many technological advances in terms of both quantity and dimen...