In this version of HIPAM, called $HIPAM_{MO}$, the number k of (child) clusters is obtained by maximizing the silhouette width (asw). See Vinue et al. (2013) for more details.
The foundation and performance of the HIPAM algorithm is explained in hipamAnthropom
.
getBestPamsamMO(x,maxsplit,orness=0.7,type,...)
WeightsMixtureUB
and GetDistMatrix
.medoids: The cluster medoids.
clustering: The clustering partition obtained.
asw: The asw of the clustering.
num.of.clusters: Number of clusters in the final clustering.
info: List that informs about the progress of the clustering algorithm.
profiles: List that contains the asw and sesw (stardard error of the silhouette widths) profiles at each stage of the search.
metric: Dissimilarity used (called 'McCulloch' because the dissimilarity function used is that explained in McCulloch et al. (1998)).
Wit, E., and McClure, J., (2004). Statistics for Microarrays: Design, Analysis and Inference. John Wiley & Sons, Ltd.
Wit, E., and McClure, J., (2006). Statistics for Microarrays: Inference, Design and Analysis. R package version 0.1.
Pollard, K. S., and van der Laan, M. J., (2002). A method to identify significant clusters in gene expression data. Vol. II of SCI2002 Proceedings, 318--325.
McCulloch, C., Paal, B., and Ashdown, S., (1998). An optimization approach to apparel sizing, Journal of the Operational Research Society 49, 492--499.
hipamAnthropom