cluster.Description.SDA: description of clusters of symbolic objects

View source: R/cluster.Description.SDA.r

cluster.Description.SDAR Documentation

description of clusters of symbolic objects

Description

description of clusters of symbolic objects is obtained by a generalisation operation using in most cases descriptive statistics calculated separately for each cluster and each symbolic variable.

Usage

cluster.Description.SDA(table.Symbolic, clusters, precission=3)

Arguments

table.Symbolic

Symbolic data table

clusters

a vector of integers indicating the cluster to which each object is allocated

precission

Number of digits to round the results

Value

A List of cluster numbers, variable number and labels.

The description of clusters of symbolic objects which differs according to the symbolic variable type:

- for interval-valued variable:

"min value" - minimum value of the lower-bounds of intervals observed for objects belonging to the cluster

"max value" - maximum value of the upper-bounds of intervals observed for objects belonging to the cluster

- for multinominal variable:

"categories" - list of all categories of the variable observed for symbolic belonging to the cluster

- for multinominal with weights variable:

"min probabilities" - minimum weight of each category of the variable observed for objects belonging to the cluster

"max probabilities" - maximum weight of each category of the variable observed for objects belonging to the cluster

"avg probabilities" - average weight of each category of the variable calculated for objects belonging to the cluster

"sum probabilities" - sum of weights of each category of the variable calculated for objects belonging to the cluster

Author(s)

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/

References

Billard, L., Diday, E. (eds.) (2006), Symbolic Data Analysis. Conceptual Statistics and Data Mining, Wiley, Chichester.

Verde, R., Lechevallier, Y., Chavent, M. (2003), Symbolic clustering interpretation and visualization, "The Electronic Journal of Symbolic Data Analysis", Vol. 1, No 1.

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.

See Also

SClust,DClust; hclust in stats library; pam in cluster library

Examples

# LONG RUNNING - UNCOMMENT TO RUN
#data("cars",package="symbolicDA")
#y<-cars
#cl<-SClust(y, 4, iter=150)
#print(cl)
#o<-cluster.Description.SDA(y, cl)
#print(o)

symbolicDA documentation built on May 28, 2022, 1:08 a.m.