FactoClass (version 1.2.9)

stableclus: Stable clusters for cluster analysis

Description

Performs Stable Cluster Algorithm for cluster analysis, using factorial coordinates from a dudi object

Usage

stableclus(dudi,part,k.clust,ff.clus=NULL,bplot=TRUE,kmns=FALSE)

Value

object of class stableclus with the following characteristics:

cluster

vector indicating the cluster of each element.

...

Arguments

dudi

A dudi object, result of a previous factorial analysis using ade4 or FactoClass

part

Number of partitions

k.clust

Number of clusters in each partition

ff.clus

Number of clusters for the final output, if NULL it asks in the console (Default NULL)

bplot

if TRUE, prints frequencies barplot of each cluster in the product partition (Default TRUE)

kmns

if TRUE, the process of consolidating the classification is performed (Default FALSE)

Author

Carlos Andres Arias , Campo Elias Pardo

Details

Diday (1972) (cited by Lebart et al. (2006)) presented a method for cluster analysis in an attempt to solve one of the inconvinients with the kmeans algorithm, which is convergence to local optims. Stable clusters are built by performing different partitions (using kmeansW algorithmn), each one with different initial points. The groups are then formed by selecting the individuals belonging to the same cluster in every partion.

References

Arias, C. A.; Zarate, D.C. and Pardo C.E. (2009), 'Implementacion del metodo de grupos estables en el paquete FactoClass de R', in: XIX Simposio Colombiano de Estadistica. Estadisticas Oficiales Medellin Colombia, Julio 16 al 20 de 2009 Universidad Nacional de Colombia. Bogota.

Lebart, L. (2015), 'DtmVic: Data and Text Mining - Visualization, Inference, Classification. Exploratory statistical processing of complex data sets comprising both numerical and textual data.', Web. http://www.dtmvic.com/

Lebart, L., Morineau, A., Lambert, T. and Pleuvret, P. (1999), SPAD. Syst?me Pour L'Analyse des Don?es, Paris.

Lebart, L., Piron, M. and Morineau, A. (2006), Statisitique exploratoire multidimensionnelle. Visualisation et inference en fouilles de donnees, 4 edn, Dunod, Paris.

Examples

 data(ColorAdjective)
 FCcol <-FactoClass(ColorAdjective, dudi.coa,nf=6,nfcl=10,k.clust=7,scanFC = FALSE)
 acs <- FCcol$dudi
# stableclus(acs,3,3,4,TRUE,TRUE)