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FactoClass (version 1.0.9)

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)

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)

Value

  • object of class stableclus with the following characteristics:
  • clustervector indicating the cluster of each element.
  • ...

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 Medell�n Colombia, Julio 16 al 20 de 2009 Universidad Nacional de Colombia. Bogota. http://www.docentes.unal.edu.co/cepardot/docs/ Lebart, L. (2008), 'DtmVic: Data and Text Mining - Visualization, Inference, Classification. Exploratory statistical processing of complex data sets comprising both numerical and textual data.', Web. http://egsh.enst.fr/lebart/ Lebart, L., Morineau, A., Lambert, T. and Pleuvret, P. (1999), SPAD. Syst�me Pour L'Analyse des Don�es, Paris. http://www.spad.eu Lebart, L., Piron, M. and Morineau, A. (2006), Statisitique exploratoire multidimensionnelle. Visualisation et inf�rence en fouilles de donn�es, 4 edn, Dunod, Paris.

Examples

Run this code
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)

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