[Zurück]

G. Pölzlbauer, A. Rauber, M. Dittenbach:
"Graph projection techniques for Self-Organizing Maps";
Poster: European Symposium on Artificial Neural Networks, Brügge, Belgien; 27.04.2005 - 29.04.2005; in: "Proceedings of the European Symposium on Artificial Neural Networks (ESANN'05)", d-side Publications, (2005), ISBN: 2-930307-05-6; S. 533 - 538.

The Self-Organizing Map is a popular neural network
model for data analysis, for which a wide variety of visualization
techniques exists.
We present two novel techniques that take
the density of the
data into account. Our methods define
graphs resulting from nearest neighbor- and radius-based distance
calculations in data space and show 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.

http://aleph.ub.tuwien.ac.at/F?base=tuw01&func=find-c&ccl_term=AC05936234

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