Performs Stable Cluster Algorithm for cluster analysis, using factorial coordinates from a dudi object
stableclus(dudi,part,k.clust,ff.clus=NULL,bplot=TRUE,kmns=FALSE)A dudi object, result of a previous factorial analysis using ade4 or FactoClass
Number of partitions
Number of clusters in each partition
Number of clusters for the final output, if NULL it asks in the console (Default NULL)
if TRUE, prints frequencies barplot of each cluster in the product partition (Default TRUE)
if TRUE, the process of consolidating the classification is performed (Default FALSE)
object of class stableclus with the following characteristics:
vector indicating the cluster of each element.
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.
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/SimposiosEstadistica/AriasZaratePardo09.pdf
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 inf?rence en fouilles de donn?es, 4 edn, Dunod, Paris.
# NOT RUN {
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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