Least-Squares Bilinear Clustering for Three-Way Data
Pieter Schoonees [aut, cre], Patrick Groenen [ctb]
c(person(given = "Pieter", family = "Schoonees", role = c("aut",
"cre"), email = "schoonees@gmail.com"), person(given = "Patrick",
family = "Groenen", role = "ctb", email = "groenen@ese.eur.nl"))
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.
plyr, clue, grid, gridExtra, reshape2, Rcpp

plyr, clue, grid, gridExtra, reshape2, Rcpp, graphics, methods

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.