Contributions to Books:
E. Pampalk, A. Rauber, W. Merkl:
"Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps";
in: "Proceedings of the International Conference on Artificial Neural Networks (ICANN 2002)",
Several methods to visualize clusters in high-dimensional data sets using
the Self-Organizing Map (SOM) have been proposed. However, most of these
methods only focus on the information extracted from the model vectors of
This paper introduces a novel method to visualize the clusters of a SOM
based on data histograms smoothened by ranking the membership of a data
item to a unit according to its distance. The method is illustrated using a
simple 2-dimensional data set and similarities to other SOM based
visualizations and to the posterior probability distribution of the
Generative Topographic Mapping are discussed. Furthermore, the method is
evaluated on a real world data set consisting of pieces of music.
Created from the Publication Database of the Vienna University of Technology.