Model-based clustering of high-dimensional data: A review

• Published in: Computational statistics & data analysis Vol. 71; pp. 52 - 78
• Main Authors: Bouveyron, Charles, Brunet-Saumard, Camille
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2014; Elsevier
• Subjects: Clustering; Computer programs; data collection; Data processing; Dimension reduction; Flexibility; High-dimensional data; Mathematics; Model-based clustering; Parsimonious models; R package; Regularization; Software; Statistics; Statistics Theory; Subspace clustering; Variable selection; High-dimensional data; Dimension reduction; Model-based clustering; Parsimonious models; Subspace clustering; Software; Regularization; R package; Variable selection
• ISSN: 0167-9473; 1872-7352
• DOI: 10.1016/j.csda.2012.12.008

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, dimension reduction approaches, regularization-based techniques, parsimonious modeling, subspace clustering methods and clustering methods based on variable selection are reviewed. 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.