index.DB {clusterSim}R Documentation

Calculates Davies-Bouldin's index

Description

Calculates Davies-Bouldin's cluster separation measure

Usage

index.DB(x, cl, centrotypes="centroids", p=2, q=2)

Arguments

x data
cl vector of integers indicating the cluster to which each object is allocated
centrotypes "centroids" or "medoids"
p the power of the Minkowski distance between centroids or medoids of clusters: p=1 - Manhattan distance; p=2 - Euclidean distance
q the power of dispersion measure of a cluster: q=1 - the average distance of objects in the r-th cluster to the centroid or medoid of the r-th cluster; q=2 - the standard deviation of the distance of objects in the r-th cluster to the centroid or medoid of the r-th cluster

Details

See file $R_HOME\library\clusterSim\pdf\indexDB_details.pdf for further details

Value

DB Davies-Bouldin's index
r vector of maximal R values for each cluster
R R matrix $(S_r+S_s)/d_{rs}$
d matrix of distances between centroids or medoids of clusters
S vector of dispersion measures for each cluster
centers coordinates of centroids or medoids for all clusters

Author(s)

Marek Walesiak Marek.Walesiak@ae.jgora.pl, Andrzej Dudek Andrzej.Dudek@ae.jgora.pl

Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland http://www.ae.jgora.pl/keii

References

Davies, D.L., Bouldin, D.W. (1979), A cluster separation measure, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1, no. 2, 224-227.

See Also

index.G1, index.G2, index.G3, index.S, index.H, index.Gap, index.KL

Examples

#Example 1
library(clusterSim)
data(data_ratio)
cl1 <- pam(data_ratio, 4)
print(index.DB(data_ratio, cl1$clustering, centrotypes="medoids"))

#Example 2
library(clusterSim)
data(data_ratio)
cl2 <- pam(data_ratio, 5)
print(index.DB(data_ratio, cl2$clustering, centrotypes="centroids"))

[Package clusterSim version 0.34-3 Index]