fpc: cdbw – R documentation

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fpc

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.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en/

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

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)

fpc

Flexible Procedures for Clustering

v2.2-9

GPL

Authors

Christian Hennig <christian.hennig@unibo.it>

Initial release

2020-12-06