Greed enables the clustering of networks and count data such as document-term
matrix with different models. Model selection and clustering are performed in
combination by optimizing the Integrated Classification Likelihood.
Optimization is performed thanks to a combination of greedy local search and
a genetic algorithm. The main entry point is the `greed`

function
to perform the clustering, which is documented below. The package also
provides sampling functions for all the implemented DLVMs: Mixture of
Multinomials (`rmm`

), Stochastic Block Model (`rsbm`

,
`rdcsbm`

) and Latent Block Model (`rlbm`

).

greed( X, K = 20, model = find_model(X), alg = methods::new("hybrid"), verbose = FALSE )

X | data to cluster either a matrix,an array or a |
---|---|

K | initial number of cluster |

model | a generative model to fit |

alg | an optimization algorithm of class |

verbose | Boolean for verbose mode |

an `icl_path-class`

object