Median topographic maps for biomedical data sets
Machine Learning
2009-09-04 v1 Quantitative Methods
Abstract
Median clustering extends popular neural data analysis methods such as the self-organizing map or neural gas to general data structures given by a dissimilarity matrix only. This offers flexible and robust global data inspection methods which are particularly suited for a variety of data as occurs in biomedical domains. In this chapter, we give an overview about median clustering and its properties and extensions, with a particular focus on efficient implementations adapted to large scale data analysis.
Cite
@article{arxiv.0909.0638,
title = {Median topographic maps for biomedical data sets},
author = {Barbara Hammer and Alexander Hasenfuß and Fabrice Rossi},
journal= {arXiv preprint arXiv:0909.0638},
year = {2009}
}