SClust | R Documentation |
Dynamical clustering of symbolic data based on symbolic data table
SClust(table.Symbolic, cl, iter=100, variableSelection=NULL, objectSelection=NULL)
table.Symbolic |
symbolic data table |
cl |
number of clusters or vector with initial prototypes of clusters |
iter |
maximum number of iterations |
variableSelection |
vector of numbers of variables to use in clustering procedure or NULL for all variables |
objectSelection |
vector of numbers of objects to use in clustering procedure or NULL for all objects |
See file ../doc/SClust_details.pdf for further details
a vector of integers indicating the cluster to which each object is allocated
Andrzej Dudek andrzej.dudek@ue.wroc.pl, Justyna Wilk justyna.wilk@ue.wroc.pl Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland http://keii.ue.wroc.pl/symbolicDA/
Bock, H.H., Diday, E. (eds.) (2000), Analysis of Symbolic Data. Explanatory Methods for Extracting Statistical Information from Complex Data, Springer-Verlag, Berlin.
Diday, E., Noirhomme-Fraiture, M. (eds.) (2008), Symbolic Data Analysis with SODAS Software, John Wiley & Sons, Chichester, pp. 185-191.
Verde, R. (2004), Clustering Methods in Symbolic Data Analysis, In: D. Banks, L. House, E. R. McMorris, P. Arabie, W. Gaul (Eds.), Classification, clustering and Data mining applications, Springer-Verlag, Heidelberg, pp. 299-317.
Diday, E. (1971), La methode des Nuees dynamiques, Revue de Statistique Appliquee, Vol. 19-2, pp. 19-34.
Celeux, G., Diday, E., Govaert, G., Lechevallier, Y., Ralambondrainy, H. (1988), Classifcation Automatique des Donnees, Environnement Statistique et Informatique - Dunod, Gauthier-Villards, Paris.
DClust
; kmeans
in stats
library
# LONG RUNNING - UNCOMMENT TO RUN #data("cars",package="symbolicDA") #sdt<-cars #clust<-SClust(sdt, cl=3, iter=50) #print(clust)
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