R: Total separation between clusters - Internal Measure

clv.Dis {clv} | R Documentation

## 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)

[Package clv version 0.3-2.2 Index]