ClusterR (version 1.2.1)

GMM: Gaussian Mixture Model clustering

Description

Gaussian Mixture Model clustering

Usage

GMM(data, gaussian_comps = 1, dist_mode = "eucl_dist",
  seed_mode = "random_subset", km_iter = 10, em_iter = 5,
  verbose = FALSE, var_floor = 1e-10, seed = 1)

Arguments

data

matrix or data frame

gaussian_comps

the number of gaussian mixture components

dist_mode

the distance used during the seeding of initial means and k-means clustering. One of, eucl_dist, maha_dist.

seed_mode

how the initial means are seeded prior to running k-means and/or EM algorithms. One of, static_subset, random_subset, static_spread, random_spread.

km_iter

the number of iterations of the k-means algorithm

em_iter

the number of iterations of the EM algorithm

verbose

either TRUE or FALSE; enable or disable printing of progress during the k-means and EM algorithms

var_floor

the variance floor (smallest allowed value) for the diagonal covariances

seed

integer value for random number generator (RNG)

Value

a list consisting of the centroids, covariance matrix ( where each row of the matrix represents a diagonal covariance matrix), weights and the log-likelihoods for each gaussian component. In case of Error it returns the error message and the possible causes.

Details

This function is an R implementation of the 'gmm_diag' class of the Armadillo library. The only exception is that user defined parameter settings are not supported, such as seed_mode = 'keep_existing'. For probabilistic applications, better model parameters are typically learned with dist_mode set to maha_dist. For vector quantisation applications, model parameters should be learned with dist_mode set to eucl_dist, and the number of EM iterations set to zero. In general, a sufficient number of k-means and EM iterations is typically about 10. The number of training samples should be much larger than the number of Gaussians. Seeding the initial means with static_spread and random_spread can be much more time consuming than with static_subset and random_subset. The k-means and EM algorithms will run faster on multi-core machines when OpenMP is enabled in your compiler (eg. -fopenmp in GCC)

References

http://arma.sourceforge.net/docs.html

Examples

# NOT RUN {
data(dietary_survey_IBS)

dat = as.matrix(dietary_survey_IBS[, -ncol(dietary_survey_IBS)])

dat = center_scale(dat)

gmm = GMM(dat, 2, "maha_dist", "random_subset", 10, 10)

# }