best_acca: Silhouette (clustering)

In meantrix/corrp: Compute Correlations Type Analysis in Parallel

Silhouette (clustering)

Description

Determining the optimal number of cluster in the ACCA clustering using the average silhouette approach.

Usage

best_acca(m, ...)

## S3 method for class 'cmatrix'
best_acca(m, mink, maxk, maxrep = 2L, maxiter = 100L, ...)

## S3 method for class 'matrix'
best_acca(m, mink, maxk, maxrep = 2L, maxiter = 100L, ...)

Arguments

m	[matrix] correlation matrix from corr_matrix.
...	Not used. Included for S3 method consistency.
mink	[integer(1)] minimum number of clusters considered.
maxk	[integer(1)] maximum number of clusters considered.
maxrep	[integer(1)] maximum number of interactions without change in the clusters in the ACCA method.
maxiter	[integer(1)] maximum number of interactions in the ACCA method.

Value

[list]
A list which is a list that has three elements:

• silhouette.ave: The silhouettes average per k.

• k: The sequence of clusters tested.

• best.k: The optimal number of clusters.

Author(s)

Igor D.S. Siciliani, Paulo H. dos Santos

References

Leonard Kaufman; Peter J. Rousseeuw (1990). Finding groups in data : An introduction to cluster analysis. Hoboken, NJ: Wiley-Interscience. p. 87. doi:10.1002/9780470316801. ISBN 9780471878766.

Starczewski, Artur, and Adam Krzyżak. "Performance evaluation of the silhouette index. "International Conference on Artificial Intelligence and Soft Computing. Springer, Cham, 2015.

See Also

sil_acca

Examples

x <- corrp::corrp(iris)
m <- corrp::corr_matrix(x)
best_acca(m, 2, 6)

meantrix/corrp documentation built on April 17, 2025, 7:22 p.m.