cluster.BIC: BIC for subspace clustering
In psobczyk/public_varclust: Variable Clustering

Computes the value of BIC criterion for given data set and partition. In each cluster we assume that variables are spanned by few factors. Considering maximum likelihood we get that those factors are in fact principal components. Noise sigma can be computed jointly for all clusters (default), seperately for each cluster or be specified as input.

1 2	cluster.BIC(X, segmentation, max.dim, numb.clusters, sigma = NULL, estimateJointly = TRUE)

`X`	a matrix with only continuous variables
`segmentation`	a vector, segmentation for which likelihood is computed. Clusters numbers should be from range [1, numb.clusters]
`max.dim`	an integer, maximum dimension of subspace. Number of principal components that span each subspace.
`numb.clusters`	an integer, number of clusters
`sigma`	a numeric, (default is NULL) value of sigma provided by the user
`estimateJointly`	a boolean, (default value is TRUE) indicating if sigma should be estimated jointly for all clusters