lsbclust: Least-Squares Bilinear Clustering for Three-Way Data

Functions for performing least-squares bilinear clustering of three-way data. The method uses the bilinear decomposition (or bi-additive model) to model two-way matrix slices while clustering over the third way. Up to four different types of clusters are included, one for each term of the bilinear decomposition. In this way, matrices are clustered simultaneously on (a subset of) their overall means, row margins, column margins and row-column interactions. The orthogonality of the bilinear model results in separability of the joint clustering problem into four separate ones. Three of these sub-problems are specific k-means problems, while a special algorithm is implemented for the interactions. Plotting methods are provided, including biplots for the low-rank approximations of the interactions.

Version: 1.1
Depends: R (≥ 3.5), stats, ggplot2
Imports: plyr, clue, grid, gridExtra, reshape2, Rcpp, mvtnorm, graphics, methods, doParallel, foreach, parallel
LinkingTo: Rcpp
Published: 2019-04-15
Author: Pieter Schoonees [aut, cre], Patrick Groenen [ctb]
Maintainer: Pieter Schoonees <schoonees at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: lsbclust citation info
CRAN checks: lsbclust results

Downloads:

Reference manual: lsbclust.pdf
Package source: lsbclust_1.1.tar.gz
Windows binaries: r-devel: lsbclust_1.1.zip, r-release: lsbclust_1.1.zip, r-oldrel: lsbclust_1.1.zip
OS X binaries: r-release: lsbclust_1.1.tgz, r-oldrel: lsbclust_1.1.tgz
Old sources: lsbclust archive

Reverse dependencies:

Reverse imports: ccrs

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