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`

)