Model-Based Clustering of High-Dimensional Data: A review

Charles Bouveyron (1) , Camille Brunet (2)

1 MAP5 - UMR 8145 - Mathématiques Appliquées Paris 5
2 LAREMA - Laboratoire Angevin de Recherche en Mathématiques

Model-based clustering is a popular tool which is renowned for its probabilistic foundations and its flexibility. However, high-dimensional data are nowadays more and more frequent and, unfortunately, classical model-based clustering techniques show a disappointing behavior in high-dimensional spaces. This is mainly due to the fact that model-based clustering methods are dramatically over-parametrized in this case. However, high-dimensional spaces have specific characteristics which are useful for clustering and recent techniques exploit those characteristics. After having recalled the bases of model-based clustering, this article will review dimension reduction approaches, regularization-based techniques, parsimonious modeling, subspace clustering methods and clustering methods based on variable selection. Existing softwares for model-based clustering of high-dimensional data will be also reviewed and their practical use will be illustrated on real-world data sets.

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Charles Bouveyron, Camille Brunet. Model-Based Clustering of High-Dimensional Data: A review. Computational Statistics and Data Analysis, 2013, 71, pp.52-78. ⟨10.1016/j.csda.2012.12.008⟩. ⟨hal-00750909⟩