Determining the optimal number of cluster in the ACCA clustering using the average silhouette aproach.
Usage
best_acca(m, ...)
# S3 method for cmatrix
best_acca(m, mink, maxk, maxrep = 2L, maxiter = 100L, ...)
# S3 method for matrix
best_acca(m, mink, maxk, maxrep = 2L, maxiter = 100L, ...)Arguments
- m
\[
matrix(1)]
correlation matrix fromcorr_matrix.- ...
Additional arguments (TODO).
- 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.