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 fromcorr_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.
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
#>