Last data update: 2014.03.03
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R: Total separation between clusters - Internal Measure
Total separation between clusters - Internal Measure
Description
Function computes total separation between clusters.
Usage
clv.Dis(cluster.center)
Arguments
cluster.center |
numeric matrix or data.frame where columns correspond to variables and rows
cluster centers.
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Details
The definition of total separation between clusters is given by equation:
Dis = (Dmax/Dmin) * sum{forall i in 1:|C|} 1 /( sum{forall j in 1:|C|} ||vi - vj|| )
where:
|C| | - number of clusters, |
vi, vj | - centers of clusters i and j, |
Dmax | - defined as: max{||vi - vj||: vi,vj - centers of clusters }, |
Dmin | - defined as: min{||vi - vj||: vi,vj - centers of clusters }, |
||x|| | - means: sqrt(x*x').
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This value is a part of clv.SD and clv.SDbw .
Value
As result Dis value 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.SD and clv.SDbw
Examples
# load and prepare data
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
# compute Dis index
scatt <- clv.Scatt(iris.data, v.pred)
dis <- clv.Dis(scatt$cluster.center)
Results
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