This repository contains an implementation of the GMeans clustering algorithm, providing a robust and efficient way to automatically determine the optimal number of clusters in a dataset.
The GMeans algorithm is an extension of the KMeans algorithm that automatically finds the appropriate number of clusters in the data. The splitting of clusters is done by using a hypothesis test to determine if a cluster follows a Gaussian distribution.
[1] Hamerly, G., & Elkan, C.P. (2003). Learning the k in k-means. Neural Information Processing Systems.
[2] R.B. D’Augostino & M.A. Stephens, Eds., 1986, Goodness-of-Fit Techniques, Marcel Dekker.