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

Identifieur interne : 002230 ( Main/Exploration ); précédent : 002229; suivant : 002231

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

Auteurs : Elias Pampalk [Autriche] ; Andreas Rauber [Autriche] ; Dieter Merkl [Autriche]

Source :

RBID : ISTEX:7FF6D08D691E1877544616D9F2A5E119B8409A67

Abstract

Abstract: 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.

Url:
DOI: 10.1007/3-540-46084-5_141


Affiliations:


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