A Gaussian Mixture© model is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters.

One can think of mixture models as generalizing k-means clustering to incorporate information about the covariance structure of the data as well as the centers of the latent Gaussians.¹

The Gaussian Mixture node in watsonx.ai Studio exposes the core features and commonly used parameters of the Gaussian Mixture library. The node is implemented in Python.

For more information about Gaussian Mixture modeling algorithms and parameters, see Gaussian Mixture Models and Gaussian Mixture. ²

¹ "User Guide." Gaussian mixture models. Web. © 2007 - 2017. scikit-learn developers.

² Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011.