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.
Stéphane Breton
Hi Olatunji,
Here are some references of interest to address your issue:
- [Section §2.2] Raykov et al., " What to do when K-means clustering fails: a simple yet principled alternative algorithm ", 2016 - https://www.semanticscholar.org/paper/What-to-Do-When-K-Means-Clustering-Fails%3A-A-Simple-Raykov-Boukouvalas/6f04ffd4df3b41fbdd7c0fda6272bb4fc81e5288
- Su et al., " In Search of Deterministic Methods for Initializing K-Means and Gaussian Mixture Clustering ", 2006 - http://www.ece.neu.edu/fac-ece/jdy/papers/initialization-su-dy-ida07.pdf
- He et al., " Laplacian Regularized Gaussian Mixture Model for Data Clustering ", 2007 - http://people.cs.uchicago.edu/~xiaofei/TKDE2011-He.pdf
Regards
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