SD_SDbw: SD, SDbw - Internal Measures In clv: Cluster Validation Techniques

Description

Function computes SD and S_Dbw validity indices.

Usage

 ```1 2``` ```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

`clv.Scatt`, `clv.Dis` and `clv.DensBw`

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```# 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) ```

Example output

```Loading required package: cluster