R: SD, SDbw - Internal Measures

clv.SD, clv.SDbw {clv} | R Documentation

## SD, SDbw - Internal Measures

### Description

Function computes SD and S_Dbw validity indices.

### Usage

    clv.SD(scatt, dis, alfa)
    clv.SDbw(scatt, dens)

### Arguments

`scatt` | average scattering for cluster value computed using `clv.Scatt` function.
`dis` | total separation between clusters value computed using `clv.Dis` function.
`dens` | inter-cluster density value computed using `clv.DensBw` function.
`alfa` | weighting factor (normally equal to Dis(cmax) where cmax is the maximum number of input clusters).

### Details

SD validity index is defined by equation:

SD = scatt*alfa + dis

where scatt means average scattering for clusters defined in `clv.Scatt`. S_Dbw validity index is defined by equation:

S_Dbw = scatt + dens

where dens is defined in `clv.DensBw`.

### Value

As result of `clv.SD` function SD validity index is returned. As result of `clv.SDbw` function S_Dbw validity index 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.Scatt`, `clv.Dis` and `clv.DensBw`

### Examples

    # load and prepare
    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

    # prepare proper input data for SD and S_Dbw indicies
    scatt <- clv.Scatt(iris.data, v.pred)
    dis <- clv.Dis(scatt$cluster.center)
    dens.bw <- clv.DensBw(iris.data, v.pred, scatt)

    # compute  SD and S_Dbw indicies
    SD <- clv.SD(scatt$Scatt, dis, alfa=5) # alfa is equal to number of clusters
    SDbw <- clv.SDbw(scatt$Scatt, dens.bw)

[Package clv version 0.3-2.2 Index]