clv.SD, clv.SDbw {clv} | R Documentation |
SD, SDbw - Internal Measures
Description
Function computes SD and validity indices.
Usage
clv.SD(scatt, dis, alfa)
clv.SDbw(scatt, dens)
Arguments
scatt |
average scattering for cluster value computed using |
dis |
total separation between clusters value computed using |
dens |
inter-cluster density value computed using |
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
.
validity index is defined by equation:
= 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 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)