Several internal functions to clustering based on the L1 data depth
This file contains a large number of extra functions originally developed to execute the DDclust algorithm proposed by R. Jornsten in Jornsten (2004). DDclust is a method for clustering based on the L1 data depth.
Regarding the original functions, a stopping criterion and a trimmed procedure have been incorporated to NNDDVQE and NNDDVQEstart. The stopping criterion includes a tolerance which, if crossed, stops the iterations. The trimmed procedure allows us to discard the more extreme individuals (those with the lowest depth values). See TDDclust.
All these functions are therefore not solely used.
All these functions were originally created by R. Jornsten and they were available freely on http://www.stat.rutgers.edu/home/rebecka/DDcl/. However, the link to this page doesn't currently exist as a result of a website redesign.
Jornsten R., (2004). Clustering and classification based on the L1 data depth, Journal of Multivariate Analysis 90, 67–89
Vinue, G., and Ibanez, M. V., (2014). Data depth and Biclustering applied to anthropometric data. Exploring their utility in apparel design. Technical report.
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.