mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models
Luca Scrucca, Michael Fop, Thomas Brendan Murphy
Finite 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.
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- Finite Mixture Models→mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture…Legacy edge — source detail unavailable
- mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture…→Model-Based Clustering, Discriminant Analysis, and Density EstimationLegacy edge — source detail unavailable
- mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture…→Model-Based Gaussian and Non-Gaussian ClusteringLegacy edge — source detail unavailable
- mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture…→Assessing a mixture model for clustering with the integrated completed likelihoodLegacy edge — source detail unavailable
- Finite Mixture Models→Model-Based Clustering, Discriminant Analysis, and Density EstimationLegacy edge — source detail unavailable
- Finite Mixture Models→Model-Based Gaussian and Non-Gaussian ClusteringLegacy edge — source detail unavailable
- Finite Mixture Models→Assessing a mixture model for clustering with the integrated completed likelihoodLegacy edge — source detail unavailable
- Model-Based Clustering, Discriminant Analysis, and Density Estimation→Model-Based Gaussian and Non-Gaussian ClusteringLegacy edge — source detail unavailable
- Model-Based Clustering, Discriminant Analysis, and Density Estimation→Assessing a mixture model for clustering with the integrated completed likelihoodLegacy edge — source detail unavailable
- Assessing a mixture model for clustering with the integrated completed likelihood→Model-Based Gaussian and Non-Gaussian ClusteringLegacy edge — source detail unavailable
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| Paper | Year | Cited |
|---|---|---|
| Finite Mixture Models | 2000 | 7,427 |
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Topics
| Bayesian Methods and Mixture Models | Computer Science |
| Algorithms and Data Compression | Computer Science |
| Advanced Clustering Algorithms Research | Computer Science |
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