CEC: Cross-Entropy Clustering
In gmum.r: GMUM Machine Learning Group Package

Create CEC model object

CEC(x = NULL, k = 0, method.type = "standard", method.init = "kmeans++",
  params.r = 0, params.cov = matrix(0), params.centroids = NULL,
  params.mix = NULL, params.function = NULL, control.nstart = 10,
  control.eps = 0.05, control.itmax = 25, log.energy = TRUE,
  log.ncluster = TRUE, seed = NULL)

`x`	Numeric matrix of data.
`k`	Initial number of clusters.
`method.type`	Type of clustering (Gaussian family). 'diagonal' Gaussians with diagonal covariance. The clustering will try to divide the data into ellipsoid with radiuses parallel to coordinate axes 'fixed_spherical' Spherical (radial) Gaussian densities (additional parameter - radius) 'fixed_covariance' The clustering will have the tendency to divide the data into clusters resembling the unit circles in the Mahalanobis distance (additional parameter - covaraince matrix required) 'func' Own function dependent on m and sigma (additional parameter) 'standard' We divide dataset into ellipsoid-like clusters without any preferences (default) 'spherical' The clustering will try to divide the data into circles of arbitrary sizes
`method.init`	Method to initialize clusters. 'centroids' 'kmeans++' 'random'
`params.r`	Radius for spherical family.
`params.cov`	Covariance matrix for covariance family.
`params.centroids`	List of centroids.
`params.mix`	List of cluster with mixed Gaussian types.
`params.function`	User energy function
`control.nstart`	How many times to perform algorithm.
`control.eps`	What change of value should terminate algorithm.
`control.itmax`	Maximum number of iterations at each start.
`log.energy`	Records collected energy of all clusters in each iteration.
`log.ncluster`	Records number of clusters in each iteration.
`seed`	User seed

## Not run: 
CEC(k=3, x=dataset)

CEC(k=3, x=dataset, control.nstart=10, method.type='spherical', control.eps=0.05)

CEC(k=2, x=dataset, method.type='spherical', method.init='centroids',
   params.centroids=list(c(-0.5,0.5),c(0,0)))

CEC(k=5, x=dataset, method.type='fixed_spherical', params.r=0.01,
   control.nstart=10, control.eps=0.07)

CEC(k=5, x=dataset, method.type='fixed_covariance',
   params.cov=matrix(c(0.03,0,0,0.01),2), control.nstart=10, control.eps=0.06)

CEC(k=1, x=dataset, method.type='func',
   params.function='name_of_my_own_function')

fixed_spherical_cluster_param = list(method.type = 'fixed_spherical', params.r = 0.001),
covariance_cluster_param = list(method.type = 'fixed_covariance',
   params.cov=matrix(c(0.05, 0, 0, 0.001), 2))
CEC(x = dataset, params.mix = list(covariance_cluster_param,
   fixed_spherical_cluster_param, fixed_spherical_cluster_param,
   fixed_spherical_cluster_param, fixed_spherical_cluster_param), control.nstart = 10)

p1 = list(method.type='spherical', k=3)
p2 = list(method.type='diagonal', k=2)
CEC(x=dataset, params.mix=list(p1, p2))

## End(Not run)