A new approach to hierarchical clustering and structuring of data with Self-Organizing Maps

Article type: Research Article

Authors: Pampalk, Elias^{a; *} | Widmer, Gerhard^{a; b} | Chan, Alvin^c

Affiliations: [a] Austrian Research Institute for Artificial Intelligence (OeFAI), Schottengasse 3, A-1010 Vienna, Austria. E-mail: [email protected], [email protected] | [b] Department of Medical Cybernetics and Artificial Intelligence, University of Vienna, Austria | [c] DSO National Laboratories, 20 Science Park Drive, Singapore 118230. E-mail: [email protected]

Correspondence: [*] Corresponding author: Tel.: +43 1 5336112 21; Fax: +43 1 5336112 77

Abstract: The Self-Organizing Map (SOM) is a powerful tool for exploratory data analysis which has been employed in a wide range of data mining applications. We present a novel approach to reveal the inherent hierarchical structure of data using multiple SOMs together with heuristics which optimize the stability. In particular, we address shortcomings of the Growing Hierarchical Self-Organizing Map (GHSOM) regarding the decision which areas in the hierarchical structure need to be represented by a finer granularity and which areas do not. We introduce the Tension and Mapping Ratio extension to exploit specific characteristics of the SOM based on the topology preservation. As a main result, in contrast to the GHSOM, the inherent hierarchical structure of the data is revealed without requiring the user to define a threshold parameter which controls the map sizes of the individual SOMs. We evaluate our approach using data from real-world data mining projects in the music domain.

Keywords: exploratory data analysis, growing hierarchical self-organizing maps, tension and mapping ratio

DOI: 10.3233/IDA-2004-8203

Journal: Intelligent Data Analysis, vol. 8, no. 2, pp. 131-149, 2004