Model-based clustering of high-dimensional data: A review
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...
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| Published in | Computational statistics & data analysis Vol. 71; pp. 52 - 78 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.03.2014
Elsevier |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| Author | Bouveyron, Charles Brunet-Saumard, Camille |
| Author_xml | – sequence: 1 givenname: Charles surname: Bouveyron fullname: Bouveyron, Charles organization: Laboratoire SAMM, EA 4543, Université Paris 1 Panthéon-Sorbonne, France – sequence: 2 givenname: Camille surname: Brunet-Saumard fullname: Brunet-Saumard, Camille email: camille.brunet@gmail.com organization: Laboratoire LAREMA, UMR CNRS 6093, Université d’Angers, France |
| BackLink | https://hal.science/hal-00750909$$DView record in HAL |
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| Snippet | Model-based clustering is a popular tool which is renowned for its probabilistic foundations and its flexibility. However, high-dimensional data are nowadays... |
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| SubjectTerms | 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 |
| Title | Model-based clustering of high-dimensional data: A review |
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