Finds representative objects for the border of a cluster and the
within-cluster variance as defined in the framework of the `cdbw`

cluster validation index (and meant to be used in that context).

```
findrep(x,xcen,clustering,cluster,r,p=ncol(x),n=nrow(x),
nc=sum(clustering==cluster))
```

x

matrix. Euclidean dataset.

xcen

mean vector of cluster.

clustering

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

;
indicating the cluster for each observation.

cluster

integer. Number of cluster to be treated.

r

integer. Number of representatives.

p

integer. Number of dimensions.

n

integer. Number of observations.

nc

integer. Number of observations in `cluster`

.

List with components

vector of index of representatives (out of all observations).

vector of index of representatives (out of only the
observations in `cluster`

).

number of representatives (this can be smaller than
`r`

if fewer pairwise different observations are in
`cluster`

.

estimated average within-cluster squared distance to mean.

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]) findrep(iriss,colMeans(iriss),irisc,cluster=1,r=2) # }