Introduction to Cross-Entropy Clustering The R Package CEC

TL;DR

This paper introduces the R Package CEC, which implements cross-entropy clustering, combining the efficiency of k-means with the flexibility of Gaussian mixtures, and provides a practical tutorial for users.

Contribution

It presents a comprehensive tutorial and functions for the CEC method in R, facilitating its application in clustering tasks.

Findings

CEC offers faster clustering with fewer unnecessary clusters.

The package supports various Gaussian mixture models.

It simplifies the application of cross-entropy clustering in R.

Abstract

The R Package CEC performs clustering based on the cross-entropy clustering (CEC) method, which was recently developed with the use of information theory. The main advantage of CEC is that it combines the speed and simplicity of $k$-means with the ability to use various Gaussian mixture models and reduce unnecessary clusters. In this work we present a practical tutorial to CEC based on the R Package CEC. Functions are provided to encompass the whole process of clustering.