most citedSelecting the number of clusters, clustering models, and algorithms. A unifying approach based on the quadratic discriminant score

14 citations · 14 across the 3 of their papers we have counts for

collaborators

3 papers

stat.ME2026

A Geometry-Aware Framework for Clustering Cylindrical Data

Giuseppe Pandolfo, Luca Coraggio, Antonio D'Ambrosio

Cylindrical data pair an angle with a linear measurement. Clustering that ignores the periodicity of the angle breaks up groups lying across its origin. We formulate the K-means al…

stat.ME2026

Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures

Emilie Eliseussen, Haakon Muggerud, Luca Coraggio +3

With the increasing availability of ranking data, there has been a growing demand for appropriate unsupervised rank-based inferential frameworks capable of handling high-dimensiona…

stat.ML2021★ 14 cited

Selecting the number of clusters, clustering models, and algorithms. A unifying approach based on the quadratic discriminant score

Luca Coraggio, Pietro Coretto

Cluster analysis requires many decisions: the clustering method and the implied reference model, the number of clusters and, often, several hyper-parameters and algorithms' tunings…