Active Function Cross-Entropy Clustering

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) .


afCEC

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)

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Reference manual

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install.packages("afCEC")

1.0.2 by Krzysztof Byrski, a year ago


https://github.com/GeigenPrinzipal/afCEC


Browse source code at https://github.com/cran/afCEC


Authors: Krzysztof Byrski [aut, cre] , Przemyslaw Spurek [ctb]


Documentation:   PDF Manual  


GPL (>= 2) license


Depends on graphics, rgl

Linking to Rcpp, RcppArmadillo


See at CRAN