clv.SD, clv.SDbw | R Documentation |
Function computes SD and \textrm{S\_Dbw}
validity indices.
clv.SD(scatt, dis, alfa)
clv.SDbw(scatt, dens)
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). |
SD validity index is defined by equation:
SD = scatt*alfa + dis
where scatt means average scattering for clusters defined in clv.Scatt
.
\textrm{S\_Dbw}
validity index is defined by equation:
\textrm{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 \textrm{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)
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