lsbclust (version 1.1)

int.lsbclust: Interaction Clustering in Least Squares Bilinear Clustering

Description

This function implements the interaction clustering part of the Least Squares Bilinear Clustering method of Schoonees, Groenen and Van de Velden (2014).

Usage

int.lsbclust(data, margin = 3L, delta, nclust, ndim = 2,
  fixed = c("none", "rows", "columns"), nstart = 50, starts = NULL,
  alpha = 0.5, parallel = FALSE, mc.cores = detectCores() - 1,
  maxit = 100, verbose = 1, method = "diag", minsize = 3L,
  return_data = FALSE)

Arguments

data

A three-way array representing the data.

margin

An integer giving the single subscript of data over which the clustering will be applied.

delta

A four-element binary vector (logical or numeric) indicating which sum-to-zero constraints must be enforced.

nclust

An integer giving the desired number of clusters. If it is a vector, the algorithm will be run for each element.

ndim

The required rank for the approximation of the interactions (a scalar).

fixed

One of "none", "rows" or "columns" indicating whether to fix neither sets of coordinates, or whether to fix the row or column coordinates across clusters respectively. If a vector is supplied, only the first element will be used.

nstart

The number of random starts to use.

starts

A list containing starting configurations for the cluster membership vector. If not supplied, random initializations will be generated.

alpha

Numeric value in [0, 1] which determines how the singular values are distributed between rows and columns.

parallel

Logical indicating whether to parallelize over different starts or not.

mc.cores

The number of cores to use in case parallel = TRUE, passed to makeCluster.

maxit

The maximum number of iterations allowed.

verbose

Integer controlling the amount of information printed: 0 = no information, 1 = Information on random starts and progress, and 2 = information is printed after each iteration for the interaction clustering.

method

The method for calculating cluster agreement across random starts, passed on to cl_agreement. None is calculated when set to NULL.

minsize

Integer giving the minimum size of cluster to uphold when reinitializing empty clusters.

return_data

Logical indicating whether to include the data in the return value or not

Value

An object of class int.lsb

Examples

# NOT RUN {
data("supermarkets")
out <- int.lsbclust(data = supermarkets, margin = 3, delta = c(1,1,0,0), nclust = 4, ndim = 2, 
           fixed = "rows", nstart = 1, alpha = 0)
# }