Talks and Poster Presentations (with Proceedings-Entry):

G. Pölzlbauer, A. Rauber, M. Dittenbach:
"Advanced Visualization Techniques for Self-organizing Maps with Graph-Based Methods";
Talk: 2nd International Symposium on Neural Networks, ISNN 2005, Chongqing, China; 2005-05-30 - 2005-06-01; in: "Advances in Neural Networks - ISNN 2005, Part II", Springer, Lecture Notes in Computer Science 3497 (2005), ISBN: 3-540-25913-9; 75 - 80.

English abstract:
The Self-Organizing Map is a popular neural network
model for data analysis, for which a wide variety of visualization
techniques exists.
We present a novel technique that takes
the density of the
data into account. Our method defines
graphs resulting from nearest neighbor- and radius-based distance
calculations in data space and shows projections of
these graph structures on the map. It can then
be observed how relations between the data are preserved by the
projection, yielding interesting insights into
the topology of the mapping, and helping to identify
outliers as well as dense regions.

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Created from the Publication Database of the Vienna University of Technology.