Last data update: 2014.03.03
R: 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
Author(s)
Markos, A. amarkos@gmail.com , Iodice D'Enza, A. iodicede@gmail.com and Van de Velden, M. vandevelden@ese.eur.nl
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)
Results