Automatic Labeling of Self-Organizing Maps:
    Making a Treasure--Map Reveal its Secrets

	 Andreas Rauber and Dieter Merkl
	Institut f{\"u}r Softwaretechnik, 
           Technische Universitaet Wien,
      Resselgasse 3/188, A--1040 Wien, Austria
	{andi, dieter}@ifs.tuwien.ac.at
Abstract:

Self-organizing maps are an unsupervised neural network model which 
lends itself to the cluster analysis of high-dimensional input data.
However, interpreting a trained map proves to be difficult because the 
features responsible for a specific cluster assignment are not evident 
from the resulting map representation. 
In this paper we present our {\em LabelSOM} approach for automatically 
labeling a trained self-organizing map with the features of the input 
data that are the most relevant ones for the assignment of a set of 
input data to a particular cluster. 
The resulting labeled map allows the user to better understand the 
structure and the information available in the map and the reason 
for a specific map organization, especially when only little prior 
information on the data set and its characteristics is available.

Keywords: Data Visualization, Neural Networks, Dimensionality 
          Reduction, Text Data Mining, Cluster Analysis

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A. Rauber and D. Merkl.
Automatic Labeling of Self-Organizing Maps: Making a Treasure-Map Reveal its Secrets
In Proceedings of the 3. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'99), Bejing, China, April 26--28, 1999.
LNCS / Lecture Notes in Artificial Intelligence, LNAI 1574, pp. 228 - 237, Springer Verlag