T3Clusf: T3Clusf: Tucker3 Fuzzy Cluster Analysis

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

Description

This is an implementation of the T3Clusf algorithm of Rocci & Vichi (2005).

Usage

T3Clusf(X, Q, R = Q, G = 2, margin = 3L, alpha = 1, eps = 1e-08,
  maxit = 100L, verbose = 1, nstart = 1L, parallel = TRUE,
  mc.cores = detectCores() - 1L, minsize = 3L)

Arguments

X	Three-way data array, with no missing values.
Q	Integer giving the number of dimensions required for mode B (variables). This is the first mode of the array, excluding the mode clustered over (see margin).
R	Integer giving the number of dimensions required for mode C (occasions). This is the second mode of the array, excluding the mode clustered over (see margin).
G	Integer giving the number of clusters required.
margin	Integer giving the margin of the array to cluster over. The remaining two modes, in the original order, corresponds to Q and R.
alpha	Numeric value giving the fuzziness parameter.
eps	Small numeric value giving the empirical convergence threshold.
maxit	Integer giving the maximum number of iterations allowed.
verbose	Integer giving the number of iterations after which the loss values are printed.
nstart	Integer giving the number of random starts required.
parallel	Logical indicating whether to parallelize over random starts if nstart > 1.
mc.cores	Argument passed to makeCluster.
minsize	Integer giving the minimum size of cluster to uphold when reinitializing empty clusters.

References

Rocci, R., & Vichi, M. (2005). Three-mode component analysis with crisp or fuzzy partition of units. Psychometrika, 70(4), 715-736.

Examples

data("dcars")
set.seed(13)
res <- T3Clusf(X = carray(dcars), Q = 3, R = 2, G = 3, alpha = 1)

lsbclust documentation built on April 20, 2018, 1:03 a.m.