Clustering and Model Selection with the Integrated Classification Likelihood
Latent Class Analysis Model Prior class
Latent Class Analysis fit results class
Abstract optimization algorithm class
Display the list of every currently available optimization algorithm
Display the list of every currently available DLVM
Method to extract the clustering results from an IclFit-class object
Extract parameters from an DcLbmFit-class object
Extract parameters from an DcSbmFit-class object
Extract mixture parameters from DiagGmmFit-class object
Extract mixture parameters from GmmFit-class object
Extract parameters from an IclFit-class object
Extract parameters from an LcaFit-class object
Extract parameters from an MoMFit-class object
Extract mixture parameters from MoRFit-class object using MAP estima...
Extract parameters from an MultSbmFit-class object
Extract parameters from an SbmFit-class object
Combined Models classes
Combined Models fit results class
Combined Models hierarchical fit results class
Method to cut a DcLbmPath solution to a desired number of cluster
Generic method to cut a path solution to a desired number of cluster
Degree Corrected Latent Block Model for bipartite graph class
Degree corrected Latent Block Model fit results class
Degree corrected Latent Block Model hierarchical fit results class
Degree Corrected Stochastic Block Model Prior class
Degree Corrected Stochastic Block Model fit results class
Degree Corrected Stochastic Block Model hierarchical fit results class
Diagonal Gaussian Mixture Model Prior description class
Diagonal Gaussian mixture model fit results class
Diagonal Gaussian mixture model hierarchical fit results class
Abstract class to represent a generative model for co-clustering
Abstract class to represent a generative model for clustering
Extract a part of a CombinedModelsPath-class object
Genetic optimization algorithm
Gaussian Mixture Model Prior description class
Gaussian mixture model fit results class
Make a matrix of plots with a given data and gmm fitted parameters
Gaussian mixture model hierarchical fit results class
Model based hierarchical clustering
Compute the entropy of a discrete sample
Hybrid optimization algorithm
Generic method to extract the ICL value from an IclFit-class object
Abstract class to represent a clustering result
Abstract class to represent a hierarchical clustering result
Generic method to get the number of clusters from an IclFit-class ob...
Latent Class Analysis hierarchical fit results class
Compute the mutual information of two discrete samples
Mixture of Multinomial Model Prior description class
Mixture of Multinomial fit results class
Mixture of Multinomial hierarchical fit results class
Multivariate mixture of regression Prior model description class
Clustering with a multivariate mixture of regression model fit results...
Multivariate mixture of regression model hierarchical fit results clas...
Greedy algorithm with multiple start class
Multinomial Stochastic Block Model Prior class
Multinomial Stochastic Block Model fit results class
Multinomial Stochastic Block Model hierarchical fit results class
Compute the normalized mutual information of two discrete samples
Plot a DcLbmFit-class
Plot a DcLbmPath-class
Plot a DcSbmFit-class object
Plot a DiagGmmFit-class object
Plot a GmmFit-class object
Plot an IclPath-class object
Plot a LcaFit-class object
Plot a MoMFit-class object
Plot a MultSbmFit-class object
Plot a SbmFit-class object
Generic method to extract the prior used to fit IclFit-class object
Generates graph adjacency matrix using a degree corrected SBM
Generate a data matrix using a Latent Block Model
Generate data from lca model
Generate data using a Multinomial Mixture
Generate data from a mixture of regression model
Generate a graph adjacency matrix using a Stochastic Block Model
Generate a graph adjacency matrix using a Stochastic Block Model
Stochastic Block Model Prior class
Stochastic Block Model fit results class
Stochastic Block Model hierarchical fit results class
Greedy algorithm with seeded initialization
Show an IclPath object
Regularized spectral clustering
Convert a binary adjacency matrix with missing value to a cube
An ensemble of algorithms that enable the clustering of networks and data matrices (such as counts, categorical or continuous) with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see <arXiv:2002:11577> for more details).
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