Active function cross-entropy clustering partitions the n-dimensional data into the clusters by finding the parameters of the mixed generalized multivariate normal distribution, that optimally approximates the scattering of the data in the n-dimensional space, whose density function is of the form: p_1*N(mi_1,^sigma_1,sigma_1,f_1)+...+p_k*N(mi_k,^sigma_k,sigma_k,f_k). The above-mentioned generalization is performed by introducing so called "f-adapted Gaussian densities" (i.e. the ordinary Gaussian densities adapted by the "active function"). Additionally, the active function cross-entropy clustering performs the automatic reduction of the unnecessary clusters. For more information please refer to P. Spurek, J. Tabor, K.Byrski, "Active function Cross-Entropy Clustering" (2017)

Active function cross-entropy clustering partitions the n-dimensional data into the clusters by finding the parameters of the mixed generalized multivariate normal distribution, that optimally approximates the scattering of the data in the n-dimensional space, whose density. The above-mentioned generalization is performed by introducing so called "f-adapted Gaussian densities" (i.e. the ordinary Gaussian densities adapted by the "active function"). Additionally, the active function cross-entropy clustering performs the automatic reduction of the unnecessary clusters. For more information please refer to P. Spurek, J. Tabor, K.Byrski, "Active function Cross-Entropy Clustering" (2017) . The afCEC package is a part of CRAN repository and it can be installed by the following command:

`install.packages("afCEC")library("afCEC")`

The basic usage comes down to the function `afCEC`

with two required arguments: input data (`points`

) and the initial number of centers (`maxClusters`

):

`afCEC (points= , maxClusters= )`

Below, a simple session with **R** is presented, where the component
(waiting) of the Old Faithful dataset is split into two clusters:

`library(afCEC)data(fire)plot(fire, asp=1, pch=20) result <- afCEC(fire, 5, numberOfStarts=10);print(result)plot(result)`

As the main result, afCEC returns data cluster membership `cec$cluster`

. The following parameters of
clusters can be obtained as well:

- means (
`result$means`

) - covariances (
`result$covariances`

) - cardinalities (
`result$cardinalities`

)