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

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

Function computes SD and S_Dbw validity indices.

1 2	clv.SD(scatt, dis, alfa) clv.SDbw(scatt, dens)

`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).

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.

As result of clv.SD function SD validity index is returned. As result of clv.SDbw function S_Dbw validity index is returned.

Lukasz Nieweglowski

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

clv.Scatt, clv.Dis and clv.DensBw

# 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)