distcritmulti: Distance based validity criteria for large data sets

In fpc: Flexible Procedures for Clustering

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 [e-mail] 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.

See Also

cluster.stats, silhouette

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 documentation built on Sept. 24, 2024, 9:07 a.m.