Talks and Poster Presentations (with Proceedings-Entry):
G. Pölzlbauer, M. Dittenbach, A. Rauber:
"A visualization technique for self-organizing maps with vector fields to obtain the cluster structure at desired levels of detail";
Talk: IEEE International Joint Conference on Neural Networks (IJCNN),
Montréal, Québec, Canada;
- 2005-08-04; in: "Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 2005)",
IEEE Computer Society,
Self-Organizing Maps (SOMs) are a prominent tool for exploratory data analysis. One core task within the utilization of SOMs is the identification of the cluster structure on the map for which several visualization methods have been proposed, yet different application domains may require additional representation of the cluster structure. In this paper, we propose such a method based on pairwise distance calculation. It can be plotted on top of the map lattice with arrows that point to the closest cluster center. A parameter is provided that determines the granularity of the clustering. We provide experimental results and discuss the general applicability of our method, along with a comparison to related techniques.
Online library catalogue of the TU Vienna:
Project Head A Min Tjoa:
Created from the Publication Database of the Vienna University of Technology.