Dis: Total separation between clusters - Internal Measure

In clv: Cluster Validation Techniques

Total separation between clusters - Internal Measure

Description

Function computes total separation between clusters.

Usage

clv.Dis(cluster.center)

Arguments

cluster.center

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

Details

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.

Value

As result Dis value is returned.

Author(s)

Lukasz Nieweglowski

References

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

See Also

clv.SD and clv.SDbw

Examples

# 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)

clv documentation built on Sept. 28, 2023, 9:06 a.m.