Median topographic maps for biomedical data sets

TL;DR

This paper discusses median clustering techniques, extending neural data analysis methods to dissimilarity data, with a focus on efficient large-scale biomedical data analysis.

Contribution

It provides an overview of median clustering, its properties, extensions, and efficient implementations for large-scale biomedical data analysis.

Findings

Median clustering offers robust data inspection.

Extensions improve applicability to various data types.

Efficient algorithms enable large-scale analysis.

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.