ClusterR (version 1.3.3)

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,
  full_covariance_matrices = FALSE
)

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.

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)

full_covariance_matrices

a boolean. If FALSE "diagonal" covariance matrices (i.e. in each covariance matrix, all entries outside the main diagonal are assumed to be zero) otherwise "full" covariance matrices will be returned. Be aware in case of "full" covariance matrices a cube (3-dimensional) rather than a matrix for the output "covariance_matrices" value will be returned.

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

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)