Description Usage Arguments Value Note Author(s) References See Also

View source: R/getBestPamsamMO.R

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`

.

1 | ```
getBestPamsamMO(data,maxsplit,orness=0.7,type,ah,verbose,...)
``` |

`data` |
Data to be clustered. |

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

`orness` |
Quantity to measure the degree to which the aggregation is like a min or max operation. See |

`type` |
Option 'MO' for using $HIPAM_MO$. |

`ah` |
Constants that define the |

`verbose` |
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 http://www.math.rug.nl/~ernst/book/smida.html.

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. http://www.math.rug.nl/~ernst/book/smida.html.

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.

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