Corpus ID: 123709149

MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering †

@inproceedings{Fraley2007MCLUSTV3,
  title={MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering †},
  author={C. Fraley and A. Raftery},
  year={2007}
}
MCLUST is a contributed R package for normal mixture modeling and model-based clustering. It provides functions for parameter estimation via the EM algorithm for normal mixture models with a variety of covariance structures, and functions for simulation from these models. Also included are functions that combine model-based hierarchical clustering, EM for mixture estimation and the Bayesian Information Criterion (BIC) in comprehensive strategies for clustering, density estimation and… Expand
474 Citations
Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering
  • 359
  • PDF
Mixture model averaging for clustering
  • 19
  • PDF
Extending mixtures of multivariate t-factor analyzers
  • 99
  • PDF
Mixture model selection via hierarchical BIC
  • 13
  • PDF
Using conditional independence for parsimonious model-based Gaussian clustering
  • 7
Genetic Algorithms for Subset Selection in Model-Based Clustering
  • 23
  • PDF
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 39 REFERENCES
Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering
  • 359
  • PDF
Model-based Gaussian and non-Gaussian clustering
  • 2,128
  • PDF
Model-Based Clustering, Discriminant Analysis, and Density Estimation
  • 3,554
  • PDF
How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
  • 2,363
  • PDF
Gaussian parsimonious clustering models
  • 747
  • Highly Influential
  • PDF
Model-based Methods of Classification: Using the mclust Software in Chemometrics
  • 234
  • PDF
MCLUST: Software for Model-Based Cluster Analysis
  • 489
  • PDF
Incremental Model-Based Clustering for Large Datasets With Small Clusters
  • 62
  • PDF
Model-Based Clustering for Image Segmentation and Large Datasets via Sampling
  • 68
  • PDF
...
1
2
3
4
...