CrossEntropyClustering {FCPS}

Cross-Entropy Clustering

Description

Cross-entropy clustering published by [Tabor/Spurek, 2014] and implemented by [Spurek et al., 2017].

Usage

CrossEntropyClustering(Data, ClusterNo,PlotIt=FALSE,...)

Arguments

Data

[1:n,1:d] matrix of dataset to be clustered. It consists of n cases of d-dimensional data points. Every case has d attributes, variables or features.

ClusterNo

A number k which defines k different clusters to be built by the algorithm.

PlotIt

Default: FALSE, If TRUE plots the first three dimensions of the dataset with colored three-dimensional data points defined by the clustering stored in Cls

...

Further arguments to be set for the clustering algorithm, if not set, default arguments are used.

Details

Contrary to most of the other implemented algorithms in this package, the results on the easiest clustering challenge of Hepta are unstable for cross-entropy clustering in the sense that the clustering is not always correct. Reproducibilty experiments should be performed (see [Tabor/Spurek, 2014]).

Value

List of

Cls

[1:n] numerical vector with n numbers defining the classification as the main output of the clustering algorithm. It has k unique numbers representing the arbitrary labels of the clustering.

Object

Object defined by clustering algorithm as the other output of this algorithm

Author(s)

Michael Thrun

References

[Spurek et al., 2017] Spurek, P., Kamieniecki, K., Tabor, J., Misztal, K., & Śmieja, M.: R package cec, Neurocomputing, Vol. 237, pp. 410-413. 2017.

[Tabor/Spurek, 2014] Tabor, J., & Spurek, P.: Cross-entropy clustering, Pattern Recognition, Vol. 47(9), pp. 3046-3059. 2014.

Examples

data('Hepta')
out=CrossEntropyClustering(Hepta$Data,ClusterNo=7,PlotIt=FALSE)