Introduction to Cross-Entropy Clustering The R Package CEC
This provides a practical tool for data analysts in statistics and machine learning, but it is incremental as it packages an existing method.
The paper introduces an R package implementing cross-entropy clustering (CEC), which addresses clustering by combining the speed of k-means with the flexibility of Gaussian mixture models and automatic cluster reduction.
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.