Cluster Visualization

Spectral Clustering
http://de.wikipedia.org/wiki/Clusteranalyse#Spectral_Clustering
EigenCluster -> http://www-math.mit.edu/cluster/ !!
(with Visualization of Clustering of the Search Results)

Multiview Clustering
http://de.wikipedia.org/wiki/Clusteranalyse#Multiview_Clustering

A Simulation of a Kohonen map for solving a 'travelling salesman' problem (developed by the TU Wien): http://www.vias.org/simulations/simusoft_travsalm.html
more information on SOMs

Prof Teuvo Kohonen's website

WebSOM:
Self-Organizing Maps for Internet Exploration
an example map of one million newsgroup items

Book on Self-Organizing Maps by Kohonen
The SOM solves difficult high-dimensional and nonlinear problems...A new area is the organization of very large document collections.

a JavaApplet demonstrating the SOM-learning of a 2d-square
(more applets by Rob Saunders can be found here) Neural Networks Tutorial with Java Applets !
DemoGNG, a Java applet, implementing several methods related to competitive learning.

Since one task will be to take a look at various existing technics in the field of bioinformatics, i'm digging through some introductionary materials at wikipedia:
* DNA Microarray: Detecting differences between two sample groups
* Sequence Alignment: Finding similar sequences
* EST = Expressed Sequence Tag (better)

* A Bioinformatics Journal (Oxford)

Application of Clustering:
* Grouping of genes with related expression patterns
* Grouping homologous sequences into gene families

some wikipedia pointers for later reading:
* http://en.wikipedia.org/wiki/Data_clustering
* http://en.wikipedia.org/wiki/Self-organizing_map
* http://en.wikipedia.org/wiki/Artificial_neural_network
* http://en.wikipedia.org/wiki/Formal_concept_analysis
resp. their (differing) german equivalents:
* http://de.wikipedia.org/wiki/Clusteranalyse
* http://de.wikipedia.org/wiki/Self-Organizing_Maps (!)
* http://de.wikipedia.org/wiki/Neuronales_Netz

The two authors of "Relationship-Based Clustering and Visualization for High-Dimensional Data Mining":
http://www.strehl.com
http://www.lans.ece.utexas.edu/~ghosh/