cluster.Description: Descriptive statistics calculated separately for each cluster...

In clusterSim: Searching for Optimal Clustering Procedure for a Data Set

Descriptive statistics calculated separately for each cluster and variable

Description

Descriptive statistics calculated separately for each cluster and variable: arithmetic mean and standard deviation, median and median absolute deviation, mode

Usage

cluster.Description(x, cl, sdType="sample",precission=4,modeAggregationChar=";")

Arguments

x	matrix or dataset
cl	a vector of integers indicating the cluster to which each object is allocated
sdType	type of standard deviation: for "sample" (n-1) or for "population" (n)
precission	Number of digits on the right side of decimal mark sign
modeAggregationChar	Character used for aggregation of mode values (if more than one value of mode appear in variable)

Value

Three-dimensional array:

First dimension contains cluster number

Second dimension contains original coordinate (variable) number from matrix or data set

Third dimension contains number from 1 to 5:

1 - arithmetic mean

2 - standard deviation

3 - median

4 - median absolute deviation (mad)

5 - mode (value of the variable which has the largest observed frequency. This formula is applicable for nominal and ordinal data only).

For example:

desc<-cluster.Description(x,cl)

desc[2,4,2] - standard deviation of fourth coordinate of second cluster

desc[3,1,5] - mode of first coordinate (variable) of third cluster

desc[1,,] - all statistics for all dimensions (variables) of first cluster

desc[,,3] - medians of all dimensions (variables) for each cluster

Author(s)

Marek Walesiak [e-mail], Andrzej Dudek [e-mail]

Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland

See Also

cluster.Sim, mean, sd, median, mad

Examples

library(clusterSim)
data(data_ratio)
cl <- pam(data_ratio,5)
desc <- cluster.Description(data_ratio,cl$cluster)
print(desc)

clusterSim documentation built on Sept. 30, 2024, 9:15 a.m.