R package cec: Abstract Cross-Entropy Clustering (CEC) is a model-based clustering method which divides data into Gaussian-like clusters. The main advantage of CEC is that it combines the speed and simplicity of k-means with the ability of using various Gaussian models similarly to EM. Moreover, the method is capable of the automatic reduction of unnecessary clusters. In this paper we present the R Package CEC implementing CEC method.
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Michael C. Thrun, Quirin Stier: Fundamental clustering algorithms suite (2021) not zbMATH
- Śmieja, Marek; Geiger, Bernhard C.: Semi-supervised cross-entropy clustering with information bottleneck constraint (2017)