fpc (version 2.2-3)

cdbw: CDbw-index for cluster validation

Description

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

Usage

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

Arguments

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.

Value

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

cdbw

value of CDbw index (the higher the better).

cohesion

cohesion.

compactness

compactness.

sep

separation.

References

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.

Examples

# 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)
# }