fuzzyMCAk: fuzzyMCAk

Description

Implements Fuzzy Cluster MCA (Hwang, Dillon and Takane, 2010) which combines Multiple Correspondence Analysis for dimension reduction with fuzzy c-means (Bezdek, 1981) for clustering.

Usage

fuzzyMCAk(data,nclus=3,ndim=2,nstart=1)

Arguments

data

categorical dataset

nclus

number of clusters

ndim

dimensionality of the solution

nstart

number of random starts

Value

obscoord: object scores
attcoord: attribute scores
centroid: cluster centroids
cluID: hard cluster membership
U: fuzzy cluster membership
FPI: Fuzziness Performance Index
MPE: Modified Partition Entropy
criterion: optimal value of the objective function

References

Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers.

Hwang, H., Dillon, W. R. and Takane, Y. (2010). Fuzzy Cluster Multiple Correspondence Analysis. Behaviormetrika, 37(2), 111-133.

Examples

Run this code

     data(underwear)  
     outfMCAk <- fuzzyMCAk(underwear[c(1:200),c(1:2)],nclus=3,ndim=2,nstart=1)
     plotrd(outfMCAk)

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