Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4947784 | Neurocomputing | 2017 | 8 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
P. Spurek, K. Kamieniecki, J. Tabor, K. Misztal, M. Åmieja,