getBestPamsamMO: Generation of the candidate clustering partition in...

View source: R/getBestPamsamMO.R

getBestPamsamMOR Documentation

Generation of the candidate clustering partition in $HIPAM_MO$


The HIPAM algorithm starts with one large cluster and, at each level, a given (parent) cluster is partitioned using PAM.

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. (2014) for more details.

The foundation and performance of the HIPAM algorithm is explained in hipamAnthropom.





Data to be clustered.


The maximum number of clusters that any cluster can be divided when searching for the best clustering.


Quantity to measure the degree to which the aggregation is like a min or max operation. See weightsMixtureUB and getDistMatrix.


Option 'MO' for using $HIPAM_MO$.


Constants that define the ah slopes of the distance function in getDistMatrix. Given the five variables considered, this vector is c(23,28,20,25,25). This vector would be different according to the variables considered.


Boolean variable (TRUE or FALSE) to indicate whether to report information on progress.


Other arguments that may be supplied.


A list with the following elements:

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)).


This function belongs to the $HIPAM_MO$ algorithm and it is not solely used. That is why there is no section of examples in this help page. See hipamAnthropom.


This function was originally created by E. Wit et al., and it is available freely on


Vinue, G., Leon, T., Alemany, S., and Ayala, G., (2014). Looking for representative fit models for apparel sizing, Decision Support Systems 57, 22–33.

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.

See Also


Anthropometry documentation built on March 7, 2023, 6:58 p.m.