CLV_kmeans: K-means algorithm for the clustering of variables

Description

K-means algorithm for the clustering of variables. Directional or local groups may be defined. Each group of variables is associated with a latent component. Moreover external information collected on the observations or on the variables may be introduced.

Usage

CLV_kmeans(X, Xu = NULL, Xr = NULL, method, sX = TRUE, sXr = FALSE, sXu = FALSE, clust, iter.max = 20, nstart = 100, strategy = "none", rho = 0.3)

Arguments

The matrix of the variables to be clustered

The external variables associated with the columns of X

The external variables associated with the rows of X

method

The criterion to use in the cluster analysis. 1 or "directional" : the squared covariance is used as a measure of proximity (directional groups). 2 or "local" : the covariance is used as a measure of proximity (local groups)

TRUE/FALSE : standardization or not of the columns X (TRUE by default) (predefined -> cX = TRUE : column-centering of X)

sXr

TRUE/FALSE : standardization or not of the columns Xr (FALSE by default) (predefined -> cXr = TRUE : column-centering of Xr)

sXu

TRUE/FALSE : standardization or not of the columns Xu (FALSE by default) (predefined -> cXu= FALSE : no centering, Xu considered as a weight matrix)

clust

: a number i.e. the size of the partition, K, or a vector of INTEGERS i.e. the group membership of each variable in the initial partition (integer between 1 and K)

iter.max

maximal number of iteration for the consolidation (20 by default)

nstart

nb of random initialisations in the case where init is a number (100 by default)

strategy

"none" (by default), or "kplusone" (an additional cluster for the noise variables), or "sparselv" (zero loadings for the noise variables)

rho

a threshold of correlation between 0 and 1 (0.3 by default)

Value

tabres: The value of the clustering criterion at convergence. The percentage of the explained initial criterion value. The number of iterations in the partitioning algorithm.
clusters: the group's membership
comp: The latent components of the clusters
loading: if there are external variables Xr or Xu : The loadings of the external variables

Details

The initalization can be made at random, repetitively, or can be defined by the user.

The parameter "strategy" makes it possible to choose a strategy for setting aside variables that do not fit into the pattern of any cluster.

Examples

Run this code

data(apples_sh)
#local groups with external variables Xr
resclvkmYX <- CLV_kmeans(X = apples_sh$pref, Xr = apples_sh$senso,method = "local",
          sX = FALSE, sXr = TRUE, clust = 2, nstart = 20)