stability {fdm2id}

Clustering evaluation through stability

Description

Evaluation a clustering algorithm according to stability, through a bootstrap procedure.

Usage

stability(
  clusteringmethods,
  d,
  originals = NULL,
  eval = "jaccard",
  type = c("cluster", "global"),
  nsampling = 10,
  seed = NULL,
  names = NULL,
  graph = FALSE,
  ...
)

Arguments

clusteringmethods

The clustering methods to be evaluated.

d

The dataset.

originals

The original clustering.

eval

The evaluation criteria.

type

The comparison method.

nsampling

The number of bootstrap runs.

seed

A specified seed for random number generation (useful for testing different method with the same bootstap samplings).

names

Method names.

graph

Indicates wether or not a graphic is potted for each sample.

...

Parameters to be passed to the clustering algorithms.

Value

The evaluation of the clustering algorithm(s) (numeric values).

See Also

compare, intern

Examples

## Not run: 
require (datasets)
data (iris)
stability (KMEANS, iris [, -5], seed = 0, k = 3)
stability (KMEANS, iris [, -5], seed = 0, k = 3, eval = c ("jaccard", "accuracy"), type = "global")
stability (KMEANS, iris [, -5], seed = 0, k = 3, type = "cluster")
stability (KMEANS, iris [, -5], seed = 0, k = 3, eval = c ("jaccard", "accuracy"), type = "cluster")
stability (c (KMEANS, HCA), iris [, -5], seed = 0, k = 3)
stability (c (KMEANS, HCA), iris [, -5], seed = 0, k = 3,
eval = c ("jaccard", "accuracy"), type = "global")
stability (c (KMEANS, HCA), iris [, -5], seed = 0, k = 3, type = "cluster")
stability (c (KMEANS, HCA), iris [, -5], seed = 0, k = 3,
eval = c ("jaccard", "accuracy"), type = "cluster")
stability (KMEANS, iris [, -5], originals = KMEANS (iris [, -5], k = 3)$cluster, seed = 0, k = 3)
stability (KMEANS, iris [, -5], originals = KMEANS (iris [, -5], k = 3), seed = 0, k = 3)

## End(Not run)