HDclassif: An R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data

Laurent Bergé, Charles Bouveyron and Stéphane Girard

Journal of Statistical Software, 2012, vol. 046, issue i06

Abstract: This paper presents the R package HDclassif which is devoted to the clustering and the discriminant analysis of high-dimensional data. The classification methods proposed in the package result from a new parametrization of the Gaussian mixture model which combines the idea of dimension reduction and model constraints on the covariance matrices. The supervised classification method using this parametrization is called high dimensional discriminant analysis (HDDA). In a similar manner, the associated clustering method is called high dimensional data clustering (HDDC) and uses the expectation-maximization algorithm for inference. In order to correctly fit the data, both methods estimate the specific subspace and the intrinsic dimension of the groups. Due to the constraints on the covariance matrices, the number of parameters to estimate is significantly lower than other model-based methods and this allows the methods to be stable and efficient in high dimensions. Two introductory examples illustrated with R codes allow the user to discover the hdda and hddc functions. Experiments on simulated and real datasets also compare HDDC and HDDA with existing classification methods on high-dimensional datasets. HDclassif is a free software and distributed under the general public license, as part of the R software project.

Date: 2012-01-30
References: View complete reference list from CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
https://www.jstatsoft.org/index.php/jss/article/view/v046i06/v46i06.pdf
https://www.jstatsoft.org/index.php/jss/article/do ... classif_1.2.1.tar.gz
https://www.jstatsoft.org/index.php/jss/article/do ... ile/v046i06/v46i06.R

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:046:i06

DOI: 10.18637/jss.v046.i06

Access Statistics for this article

Journal of Statistical Software is currently edited by Bettina Grün, Edzer Pebesma and Achim Zeileis

More articles in Journal of Statistical Software from Foundation for Open Access Statistics
Bibliographic data for series maintained by Christopher F. Baum ().