Data topology visualization for the Self-Organizing Map

The Self-Organizing map (SOM), a powerful method for data mining and cluster extraction, is very useful for processing data of high dimensionality and complexity. Visualization methods present different aspects of the information learned by the SOM to gain insight and guide segmentation of the data. In this work, we propose a new visualization scheme that represents data topology superimposed on the SOM grid, and we show how it helps in the discovery of data structure. 1 Visualization of SOM knowledge The Self-Organizing Map (SOM) [1] is a widely and successfully used neural paradigm for clustering and data mining. Informative representation of the learned SOM's knowledge greatly aids precise capture of the cluster boundaries. This is especially important for high-dimensional and large data sets with many meaningful clusters such as in remote sensing or medical imagery, which often also have interesting rare clusters to be discovered. An impressive suite of previous works include ...

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Kadim Tasdemir, Erzsébet Merényi
• Keywords: Data Mining, Data Topology, ESANN 2006, ESANN 2007, SOM Grid