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
)

Arguments

X

data to cluster either a matrix,an array or a dgCMatrix-class

K

initial number of cluster

model

a generative model to fit sbm-class, dcsbm-class, co_dcsbm-class, mm-class,gmm-class, diaggmm-class or mvmreg-class

alg

an optimization algorithm of class hybrid-class (default), multistarts-class, seed-class or genetic-class

verbose

Boolean for verbose mode

Value

an icl_path-class object