css: Clustering Sum-of-Squares for clustering evaluation
In GMD: Generalized Minimum Distance of distributions

Description Usage Arguments Details Value See Also

Evaluation on the varaince of a clustering model using squared Euclidean distances, based on distance matrix and cluster membership.

css(dist.obj,clusters)

## Computing Sum-of-Squares given Hierarchical Clustering
## S3 method for class 'hclust'
css(dist.obj, hclust.obj=NULL,hclust.FUN=hclust,
hclust.FUN.MoreArgs=list(method="ward"),k=NULL)

`dist.obj`	a ‘dist’ object as produced by `dist` or `gdist`.
`clusters`	a vector with cluster memberships.
`k`	numeric, the upper bound of the number of clusters to compute. DEFAULT: 20 or the number of observations (if less than 20).
`hclust.obj`	a ‘hclust’ object, generated by `hclust`
`hclust.FUN`	a function, to generate a hierarchical clustering. Ignored with `hclust.obj` specified. DEFAULT: `hclust`
`hclust.FUN.MoreArgs`	a list, containing arguments that are passed to `hclust.FUN`.

Clustering Sum-of-Squares for clustering evaluation.

css returns a ‘css’ object, which is a list containing the following components

k	number of clusters
wss	`k` within-cluster sum-of-squares
totwss	total within-cluster sum-of-square
totbss	total between-cluster sum-of-square
tss	total sum of squares of the data

, and with an attribute ‘meta’ that contains the input components

dist.obj	(the input) distance matrix
clusters	(the input) cluster membership

css.hclust returns a ‘css.multi’ object, which is a data.frame containing the following columns

k	number of clusters
ev	explained variance given `k`
totbss	total between-cluster sum-of-square
tss	total sum of squares of the data

, and with an attribute ‘meta’ that contains

cmethod	the clustering method
dist.obj	(the input) distance matrix
k	(the input) number of clusters
clusters	the `hclust' object that is either by input or computed by default