Automatic Labeling of Self-Organizing Maps for Information Retrieval


Andreas Rauber, Dieter Merkl
Department of Software Technology
Vienna University of Technology
Favoritenstr. 9 - 11 / 188, A--1040 Vienna, Austria



The self-organizing map is a very popular unsupervised neural network
model for the analysis of high-dimensional input data as in information
retrieval applications. However, the interpretation of the map requires
much manual effort, especially as far as the analysis of the learned
features and the characteristics of identified clusters is concerned.
In this paper we present the LabelSOM method which, based on the
features learned by the map, automatically selects the most descriptive
features of the input patterns mapped onto a particular unit of the map,
thus making the characteristics of the various clusters within the map
explicit.We demonstrate the benefits of this approach on an example from
text classification using a real-world document archive.
In this particular case, the features correspond to keywords describing
the contents of a document.
The benefit of this approach is that the various document clusters are
characterized in terms of shared keywords, thus making it easy for the
user to explore the contents of an unknown document archive.

Keywords: text mining, unsupervised learning, self-organizing map (SOM),
neural networks, clustering, digital libraries, topic detection

---------------------------
In: Journal of Systems Research and Information Systems (JSRIS)
    Vol. 10, Nr. 10, pp. 23-45, OPA, Gordon and Breach Science Publishers, Dec. 2001