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

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 space...