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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(1)]
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(3)]
A list with: silhouette average with per k $silhouette.ave; the sequence of clusters tested $k and the optimal number of clusters $best.k.

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

Author

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

Examples


x <- corrp::corrp(iris)
m <- corrp::corr_matrix(x)
best_acca(m, 2, 6)
#> $silhouette.ave
#> [1]  0.1269651 -0.4922020 -0.1472888  0.0000000 -0.2543257
#> 
#> $k
#> [1] 2 3 4 5 6
#> 
#> $best.k
#> [1] 2
#>