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Your query term was 'number = 2002-29'
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OFAI-TR-2002-29 ( 394kB PDF file)

Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps

Elias Pampalk, Andreas Rauber, Dieter Merkl

Several methods to visualize clusters in high-dimensional data sets using the Self-Organizing Map (SOM) have been proposed. However, most of these methods only focus on the information extracted from the model vectors of the SOM. This paper introduces a novel method to visualize the clusters of a SOM based on smoothed data histograms. The method is illustrated using a simple 2-dimensional data set and similarities to other SOM based visualizations and to the posterior probability distribution of the Generative Topographic Mapping are discussed. Furthermore, the method is evaluated on a real world data set consisting of pieces of music.

Citation: Pampalk E., Rauber A., Merkl D.: Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps. Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2002-29, 2002