R: Compute internal clustering criteria

intCriteria {clusterCrit} | R Documentation

## Compute internal clustering criteria

### Description

`intCriteria` calculates various internal clustering validation or quality criteria.

### Usage

      intCriteria(traj, part, crit)

### Arguments

`traj` | `[matrix]` : the matrix of observations (trajectories).
`part` | `[vector]` : the partition vector.
`crit` | `[vector]` : a vector containing the names of the indices to compute.

### Details

The function `intCriteria` calculates internal clustering indices. The list of all the supported criteria can be obtained with the `getCriteriaNames` function.

The currently available indices are :

- `"Ball_Hall"`

- `"Banfeld_Raftery"`

- `"C_index"`

- `"Calinski_Harabasz"`

- `"Davies_Bouldin"`

- `"Det_Ratio"`

- `"Dunn"`

- `"Gamma"`

- `"G_plus"`

- `"GDI11"`

- `"GDI12"`

- `"GDI13"`

- `"GDI21"`

- `"GDI22"`

- `"GDI23"`

- `"GDI31"`

- `"GDI32"`

- `"GDI33"`

- `"GDI41"`

- `"GDI42"`

- `"GDI43"`

- `"GDI51"`

- `"GDI52"`

- `"GDI53"`

- `"Ksq_DetW"`

- `"Log_Det_Ratio"`

- `"Log_SS_Ratio"`

- `"McClain_Rao"`

- `"PBM"`

- `"Point_Biserial"`

- `"Ray_Turi"`

- `"Ratkowsky_Lance"`

- `"Scott_Symons"`

- `"SD_Scat"`

- `"SD_Dis"`

- `"S_Dbw"`

- `"Silhouette"`

- `"Tau"`

- `"Trace_W"`

- `"Trace_WiB"`

- `"Wemmert_Gancarski"`

- `"Xie_Beni"`

All the names are case insensitive and can be abbreviated. The keyword `"all"` can also be used as a shortcut to calculate all the internal indices.

The GDI (Generalized Dunn Indices) are designated by the following convention: GDImn, where the integers m (1<=m<=5) and n (1<=n<=3) correspond to the between-group and within-group distances respectively. See the vignette for a comprehensive definition of the various distances. `GDI` alone is synonym of `GDI11` and is the genuine Dunn's index.

### Value

A list containing the computed criteria, in the same order as in the `crit` argument.

### Author

Bernard Desgraupes
bernard.desgraupes@u-paris10.fr
University of Paris Ouest - Nanterre
Lab Modal'X (EA 3454)

### References

See the bibliography at the end of the vignette.

### See Also

`getCriteriaNames`, `extCriteria`, `bestCriterion`.

### Examples

    # Create some data
    x <- rbind(matrix(rnorm(100, mean = 0, sd = 0.5), ncol = 2),
               matrix(rnorm(100, mean = 1, sd = 0.5), ncol = 2),
               matrix(rnorm(100, mean = 2, sd = 0.5), ncol = 2))
    # Perform the kmeans algorithm
    cl <- kmeans(x, 3)
    # Compute all the internal indices
    intCriteria(x,cl$cluster,"all")
    # Compute some of them
    intCriteria(x,cl$cluster,c("C_index","Calinski_Harabasz","Dunn"))
    # The names are case insensitive and can be abbreviated
    intCriteria(x,cl$cluster,c("det","cal","dav"))

[Package clusterCrit version 1.2.8 Index]