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) <doi:10.1016/j.eswa.2016.12.011>.
|Author||Krzysztof Byrski [aut, cre], Przemyslaw Spurek [ctb]|
|Maintainer||Krzysztof Byrski <firstname.lastname@example.org>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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