doublekm {drclust}

Double k-means Clustering

Description

Performs simultaneous k-means partitioning on units and variables (rows and columns of the data matrix).

Usage

doublekm(Xs, K, Q, Rndstart, verbose, maxiter, tol, prep, print)

Arguments

Xs

Units x variables numeric data matrix.

K

Number of clusters for the units.

Q

Number of clusters for the variables.

Rndstart

Number of runs to be performed (Defaults is 20).

verbose

Outputs basic summary statistics for each run (1 = enabled; 0 = disabled, default option).

maxiter

Maximum number of iterations allowed (if convergence is not yet reached. Default is 100).

tol

Tolerance threshold. It is the maximum difference between the values of the objective function of two consecutive iterations such that convergence is assumed (default is 1e-6).

prep

Pre-processing of the data. 1 performs the z-score transform (default choice); 2 performs the min-max transform; 0 leaves the data un-pre-processed.

print

Prints summary statistics of the results (1 = enabled; 0 = disabled, default option).

Value

returns a list of estimates and some descriptive quantities of the final results.

U

Units x clusters membership matrix (binary and row-stochastic). Each row is a dummy variable indicating to which unit-cluster each unit has been assigned.

V

Variables x clusters membership matrix (binary and row-stochastic). Each row is a dummy variable indicating to which variable-cluster each variable has been assigned.

centers

K x Q matrix of centers containing the row means expressed in terms of column means.

totss

The total sum of squares (scalar).

withinss

Vector of within-row-cluster sum of squares, one component per cluster.

columnwise_withinss

Vector of within-column-cluster sum of squares, one component per cluster.

betweenss

Amount of deviance captured by the model (scalar).

K-size

Number of units assigned to each row-cluster (vector).

Q-size

Number of variables assigned to each column-cluster (vector).

pseudoF

Calinski-Harabasz index of the resulting (row-) partition (scalar).

loop

The index of the (best) run from which the results have been chosen.

it

the number of iterations performed during the (best) run.

Author(s)

Ionel Prunila, Maurizio Vichi

References

Vichi M. (2001) "Double k-means Clustering for Simultaneous Classification of Objects and Variables" <doi:10.1007/978-3-642-59471-7_6>

Examples

# Iris data 
# Loading the numeric variables of iris data
iris <- as.matrix(iris[,-5]) 

# double k-means with 3 unit-clusters and 2 variable-clusters
out <- doublekm(iris, K = 3, Q = 2)