R: Distance based validity criteria for large data sets

distcritmulti {fpc} | R Documentation

## 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 c.hennig@ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche

### 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")

[Package fpc version 2.1-10 Index]