Technical Reports - Query Results
Your query term was 'number = 2003-09'1 report found
- ÖFAI-TR-2003-09 (
454kB PDF file)A New Approach to Hierarchical Clustering and Structuring of Data with Self-Organizing Maps
- Elias Pampalk, Gerhard Widmer, Alvin Chan
- 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
- 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.