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

Authors:
Andreas Rauber;Dieter Merkl
Affiliations:
-;-
Venue:
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Year:
1999

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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 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.