clustrd (version 0.1.2)

iFCB: iFCB

Description

Implements iFCB (Iodice D'Enza and Palumbo, 2013) which combines Nonsymmetric Correspondence Analysis for dimension reduction with k-means for clustering.

Usage

iFCB(data,nclus,ndim,nstart=100,smartStart=F,seed=1234)

Arguments

data

categorical dataset

nclus

number of clusters

ndim

dimensionality of the solution

nstart

number of random starts

smartStart

If TRUE then starting values are obtained with k-means

seed

seed is used to set the random number seed when smartStart = FALSE

Value

obscoord

object scores

attcoord

attribute scores

centroid

cluster centroids

cluID

cluster membership

criterion

optimal value of the objective function

References

Iodice D' Enza, A. and Palumbo, F. (2013). Iterative factor clustering of binary data. Computational Statistics, 28 (2), 789-807.

See Also

MCAk, fuzzyMCAk, groupals

Examples

     data(underwear)
     attlab = c(c(1:15),"by","tr","vm","jd","ml","bn","bg","ck","a1","a2","a3")
     outiFCB <- iFCB(underwear,nclus=3,ndim=2,nstart=1,smartStart=TRUE,seed=1234)
     plotrd(outiFCB,attlabel=attlab)