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

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.