fpc: distcritmulti – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

distcritmulti

Distance based validity criteria for large data sets

Description

Approximates average silhouette width or the Pearson version of Hubert's gamma criterion by hacking the dataset into pieces and averaging the subset-wise values, see Hennig and Liao (2013).

Usage

distcritmulti(x,clustering,part=NULL,ns=10,criterion="asw",
                    fun="dist",metric="euclidean",
                     count=FALSE,seed=NULL,...)

Arguments

`x`	cases times variables data matrix.
`clustering`	vector of integers indicating the clustering.
`part`	vector of integer subset sizes; sum should be smaller or equal to the number of cases of `x`. If `NULL`, subset sizes are chosen approximately equal.
`ns`	integer. Number of subsets, only used if `part==NULL`.
`criterion`	`"asw"` or `"pearsongamma"`, specifies whether the average silhouette width or the Pearson version of Hubert's gamma is computed.
`fun`	`"dist"` or `"daisy"`, specifies which function is used for computing dissimilarities.
`metric`	passed on to `dist` (as argument `method`) or `daisy` to determine which dissimilarity is used.
`count`	logical. if `TRUE`, the subset number just processed is printed.
`seed`	integer, random seed. (If `NULL`, result depends on random numbers.)
`...`	further arguments to be passed on to `dist` or `daisy`.

Value

A list with components crit.overall,crit.sub,crit.sd,part.

`crit.overall`	value of criterion.
`crit.sub`	vector of subset-wise criterion values.
`crit.sd`	standard deviation of `crit.sub`, can be used to assess stability.
`subsets`	list of case indexes in subsets.

Author(s)

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

References

Halkidi, M., Batistakis, Y., Vazirgiannis, M. (2001) On Clustering Validation Techniques, Journal of Intelligent Information Systems, 17, 107-145.

Hennig, C. and Liao, T. (2013) How to find an appropriate clustering for mixed-type variables with application to socio-economic stratification, Journal of the Royal Statistical Society, Series C Applied Statistics, 62, 309-369.

Kaufman, L. and Rousseeuw, P.J. (1990). "Finding Groups in Data: An Introduction to Cluster Analysis". Wiley, New York.

Examples

set.seed(20000)
    options(digits=3)
    face <- rFace(50,dMoNo=2,dNoEy=0,p=2)
    clustering <- as.integer(attr(face,"grouping"))
    distcritmulti(face,clustering,ns=3,seed=100000,criterion="pearsongamma")

fpc

Flexible Procedures for Clustering

v2.2-9

GPL

Authors

Christian Hennig <christian.hennig@unibo.it>

Initial release

2020-12-06