Gaussian Mixture Models analysis & application

This little project (in italian) gives a little introduction to GMMs and then presents an application of the technique on a little dataset which goes by the name "Seeds".

Table of Content

• Latex
• Jupyter Lab Notebook
• References

Tex

Under tex subdirectory you will find .tex file, partially generated via Jupyter Lab, and the related final .pdf file.

In these documents, Gaussian Mixture Models are introduced without going too deep, but giving a proper background and explaining why we headed to this kind of technique starting from K-means. The second chapter of the pdf is just an export of the Jupyter notebook.

Jupyter

Under jupyter subdirectory you will find "Seeds" dataset in tsv format and requirements for the project to work in a Python virtual environment together with, of course, the Jupyter Lab notebook.

GMMs are applied to the dataset and a qualitative analysis is performed on the results.

License

Distributed under the MIT License. See LICENSE for more information.

References

• Christopher M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, Berlin, Heidelberg, 2006.
• Oscar Contreras Carrasco. ”Gaussian Mixture Models Explained".
• M. Charytanowicz, J. Niewczas, P. Kulczycki, P.A. Kowalski, S. Lukasik, and S. Zak. ”A Complete Gradient Clustering Algorithm for Features Analysis of X-ray Images”. Information Technologies in Biomedicine, Ewa Pietka, Jacek Kawa (eds.), 2010.
• Daniel Foley. ”Gaussian Mixture Modelling (GMM)".