Visual Explorations in Finance
Visual Explorations in Finance
Self-Organizing Maps
Information navigation on the web by clustering and summarizing query results
Information Processing and Management: an International Journal
Content-based organization and visualization of music archives
Proceedings of the tenth ACM international conference on Multimedia
Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
In Search of the Horowitz Factor: Interim Report on a Musical Discovery Project
DS '02 Proceedings of the 5th International Conference on Discovery Science
Complexity Selection of the Self-Organizing Map
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
The Growing Hierarchical Self-Organizing Map
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Psychoacoustics: Facts and Models
Psychoacoustics: Facts and Models
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
Visualizing Expressive Performance in Tempo-Loudness Space
Computer Music Journal
Aspect-Based Tagging for Collaborative Media Organization
From Web to Social Web: Discovering and Deploying User and Content Profiles
Filtering intrusion detection alarms
Cluster Computing
Binary tree time adaptive self-organizing map
Neurocomputing
Growing hierarchical principal components analysis self-organizing map
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
The growing hierarchical recurrent self organizing map for phoneme recognition
NOLISP'09 Proceedings of the 2009 international conference on Advances in Nonlinear Speech Processing
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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.