Functions for performing least-squares bilinear clustering of three-way data. The method uses the bilinear decomposition (or biadditive 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 subproblems 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.
|Depends:||R (≥ 3.2.2), stats, parallel, ggplot2|
|Imports:||plyr, clue, grid, gridExtra, reshape2, Rcpp, graphics, methods|
|Author:||Pieter Schoonees [aut, cre], Patrick Groenen [ctb]|
|Maintainer:||Pieter Schoonees <schoonees at gmail.com>|
|License:||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]|
|Citation:||lsbclust citation info|
|CRAN checks:||lsbclust results|
|Windows binaries:||r-devel: lsbclust_1.0.4.zip, r-release: lsbclust_1.0.4.zip, r-oldrel: lsbclust_1.0.4.zip|
|OS X Mavericks binaries:||r-release: lsbclust_1.0.4.tgz, r-oldrel: lsbclust_1.0.4.tgz|
|Old sources:||lsbclust archive|
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