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 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.

Install the latest version of this package by entering the following in R:
AuthorPieter Schoonees [aut, cre], Patrick Groenen [ctb]
Date of publication2016-01-05 14:18:01
MaintainerPieter Schoonees <>
LicenseGPL (>= 2)

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AssignCluster Man page
bicomp Man page Man page
ClustMeans Man page
cmat Man page
col.kmeans Man page
ComputeMeans Man page
dcars Man page
genproc Man page
indarr Man page
int.lsbclust Man page Man page Man page
KMeansW Man page
LossMat Man page
lov Man page
lsbclust Man page
lsbclust-package Man page
orc.lsbclust Man page
ovl.kmeans Man page
plot.bicomp Man page
plot.col.kmeans Man page Man page
plot.lsbclust Man page
plot.ovl.kmeans Man page
plot.row.kmeans Man page
plot.step.lsbclust Man page
print.lsbclust Man page
row.kmeans Man page
sim.lsbclust Man page
step.lsbclust Man page Man page
summary.lsbclust Man page
supermarkets Man page
T3Clusf Man page

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