cnCluster: Network Clustering

In sdnet: Soft-Discretization-Based Bayesian Network Inference

Description

Retrieving the clusters, the connected sub-networks, of a given network. Estimating the clusters from data.

Usage

 cnCluster(object)
 cnClusterSep(object, data, pert=NULL)
 cnClusterMI(data, pert=NULL, threshold=0)

Arguments

object	a catNetwork
data	a matrix in row-nodes format or a data.frame in column-nodes format
pert	a binary perturbation matrix with the dimensions of data
threshold	a numeric value

Details

The function cnCluster constructs a list of subsets of nodes of the object, each representing a connected sub-network. Isolated nodes, these are nodes not connected to any other, are not reported. Thus, every element of the output list contains at least two nodes. The function cnClusterMI clusters the nodes of the data using the pairwise mutual information and critical value threshold.

Value

A list of named nodes.

Author(s)

N. Balov

Examples

  cnet <- cnRandomCatnet(numnodes=30, maxpars=2, numcats=2)
  cnCluster(object=cnet)

sdnet documentation built on May 2, 2019, 12:43 a.m.