Dis: Total separation between clusters - Internal Measure
In clv: Cluster Validation Techniques

Description Usage Arguments Details Value Author(s) References See Also Examples

Function computes total separation between clusters.

1	clv.Dis(cluster.center)

cluster.center

numeric matrix or data.frame where columns correspond to variables and rows cluster centers.

The definition of total separation between clusters is given by equation:

Dis = (Dmax/Dmin) * sum{forall i in 1:|C|} 1 /( sum{forall j in 1:|C|} ||vi - vj|| )

where:

\|C\|	- number of clusters,
vi, vj	- centers of clusters i and j,
Dmax	- defined as: max{\|\|vi - vj\|\|: vi,vj - centers of clusters },
Dmin	- defined as: min{\|\|vi - vj\|\|: vi,vj - centers of clusters },
\|\|x\|\|	- means: sqrt(x*x').

This value is a part of clv.SD and clv.SDbw.

As result Dis value is returned.

Lukasz Nieweglowski

M. Haldiki, Y. Batistakis, M. Vazirgiannis On Clustering Validation Techniques, http://citeseer.ist.psu.edu/513619.html

clv.SD and clv.SDbw

# load and prepare data
library(clv)
data(iris)
iris.data <- iris[,1:4]

# cluster data
agnes.mod <- agnes(iris.data) # create cluster tree 
v.pred <- as.integer(cutree(agnes.mod,5)) # "cut" the tree 

# compute Dis index
scatt <- clv.Scatt(iris.data, v.pred)
dis <- clv.Dis(scatt$cluster.center)