CDbw-index for cluster validation, as defined in Halkidi and Vazirgiannis (2008), Halkidi et al. (2015).

```
cdbw(x,clustering,r=10,s=seq(0.1,0.8,by=0.1),
clusterstdev=TRUE,trace=FALSE)
```

x

something that can be coerced into a numerical matrix. Euclidean dataset.

clustering

vector of integers with length `=nrow(x)`

;
indicating the cluster for each observation.

r

integer. Number of cluster border representatives.

s

numerical vector of shrinking factors (between 0 and 1).

clusterstdev

logical. If `TRUE`

, the neighborhood radius
for intra-cluster density is the within-cluster estimated squared
distance from the mean of the cluster; otherwise it is the average of
these over all clusters.

trace

logical. If `TRUE`

, results are printed for the
steps to compute the index.

List with components (see Halkidi and Vazirgiannis (2008), Halkidi et al. (2015) for details)

value of CDbw index (the higher the better).

cohesion.

compactness.

separation.

Halkidi, M. and Vazirgiannis, M. (2008) A density-based cluster
validity approach using multi-representatives. *Pattern
Recognition Letters* 29, 773-786.

Halkidi, M., Vazirgiannis, M. and Hennig, C. (2015) Method-independent
indices for cluster validation. In C. Hennig, M. Meila, F. Murtagh,
R. Rocci (eds.) *Handbook of Cluster Analysis*, CRC
Press/Taylor `&`

Francis, Boca Raton.

# NOT RUN { options(digits=3) iriss <- as.matrix(iris[c(1:5,51:55,101:105),-5]) irisc <- as.numeric(iris[c(1:5,51:55,101:105),5]) cdbw(iriss,irisc) # }