Pietro CORETTO | Projects

This project introduces a software library for automated cluster analysis. The library incorporates data-driven methods for optimal cluster number determination and advanced algorithms' tuning for discovering approximately elliptical clusters. By providing a comprehensive toolkit for cluster selection and validation, this research aims to enhance the efficiency and accuracy of clustering tasks in

The project aims to develop computational methods for clustering methods based on elliptic-symmetric distributions. Such models are useful for discovering clusters whose internal geometry can be explained in terms of multivariate centrality and scatter parameters.

The k-means method is often referred to as nonparametric clustering method, based on the nonparametric consistency theorem proved by Pollard (1981). We want to study a similar notion of consistency for clustering methods based on mixtures models of a general class of elliptically symmetric distributions (including popular models such as the Gaussian, Student-t, and Laplace, etc.).