FisherEM-package: The FisherEM Algorithm to Simultaneously Cluster and...

In FisherEM: The FisherEM Algorithm to Simultaneously Cluster and Visualize High-Dimensional Data

Description

The FisherEM algorithm, proposed by Bouveyron & Brunet (201) <doi:10.1007/s11222-011-9249-9>, is an efficient method for the clustering of high-dimensional data. FisherEM models and clusters the data in a discriminative and low-dimensional latent subspace. It also provides a low-dimensional representation of the clustered data. A sparse version of Fisher-EM algorithm is also provided.

Details

Package:	FisherEM
Type:	Package
Version:	1.2
Date:	2012-07-09
License:	GPL-2
LazyLoad:	yes

Author(s)

Charles Bouveyron, Camille Brunet & Nicolas Jouvin.

Maintainer: Charles Bouveyron <[e-mail]>

References

Charles Bouveyron, Camille Brunet (2012), "Simultaneous model-based clustering and visualization in the Fisher discriminative subspace.", Statistics and Computing, 22(1), 301-324 <doi:10.1007/s11222-011-9249-9>.

Charles Bouveyron and Camille Brunet (2014), "Discriminative variable selection for clustering with the sparse Fisher-EM algorithm", Computational Statistics, vol. 29(3-4), pp. 489-513 <10.1007/s00180-013-0433-6>.

FisherEM documentation built on Oct. 23, 2020, 8:08 p.m.