unsupervisedClustering: Variational Bayesian inference for unsupervised clustering
In acabassi/variational-ogc: Variational Outcome-Guided Clustering

Description Usage Arguments Value References

View source: R/unsupervised_clustering.R

Variational Bayesian inference for unsupervised clustering

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unsupervisedClustering(X, K, prior, ms = F, vs = F, labels_init,
  kmeans_init = F, tol = 1e-19, maxiter = 2000, verbose = F,
  indep = T)

`X`	NxD data matrix.
`K`	(Maximum) number of clusters.
`prior`	Prior parameters (optional).
`ms`	Boolean flag that indicates whether model selection is required or not. Default is FALSE.
`vs`	Boolean flag that indicates whether variable selection is required or not. Default is FALSE.
`labels_init`	Initial cluster labels (can be empty).
`kmeans_init`	Boolean flag, which, if TRUE, initializes the cluster labels with the k-means algorithm. Default is FALSE.
`tol`	Tolerance on lower bound. Default is 10e-20.
`maxiter`	Maximum number of iterations of the VB algorithm. Default is 2000.
`verbose`	Boolean flag which, if TRUE, prints the iteration numbers. Default is FALSE.
`indep`	Boolean flag which, if TRUE, indicates that the covariates are independent. Default is TRUE.

A list containing L, the lower bound at each step of the algorithm, label, a vector containing the cluster labels, model, a list containing the trained model structure, and a vector called n_ comp which, if model selection is required, contains the number of mixture components at every step of the VB algorithm.

Pattern Recognition and Machine Learning by Christopher M. Bishop.

This function is based on the MatLab code by Mo Chen (sth4nth@gmail.com).

acabassi/variational-ogc documentation built on May 23, 2019, 2:45 p.m.