SOM-based topology visualization for interactive analysis of high-dimensional large datasets
Kadim Taşdemi̇r, Erzsébet Merényi · 2012
Low-dimensional (2or 3-dimensional) visual representations of large, highdimensional datasets with complicated cluster structures play a fundamental role in the discovery and identification of such structures. Visualization exploits the unmatched pattern recognition capability of humans for accurate and detailed cluster extraction, which is not possible with current automated methods because the latter still lack the power of the exceptional human reasoning. For explanatory and interactive visualization, a powerful tool is the use of self-organizing maps (SOMs). In general, by producing a spatially ordered set of quantization prototypes of large, higher-dimensional data, SOMs enable the visualization of various similarity information (such as prototype distances, distribution, topology) on a rigid lattice, without reducing the feature dimensionality. Information discovery further depends on the expressive power of the similarity measure and its visual representation. In this study, we compare the capabilities of our recent SOM visualization scheme, CONNvis, with prominent dimensionality reduction methods and show its superiority for visual assessment of intricate cluster structures. Machine Learning Reports http://www.techfak.uni-bielefeld.de/∼fschleif/mlr/mlr.html SOM-based topology visualization for interactive analysis of high-dimensional large datasets Kadim Tasdemira, Erzsebet Merenyib a Department of Computer Engineering, Antalya International University Universite Caddesi No: 2, Dosemealti, Antalya, 07190, Turkey [email protected] bDept. of Statistics and Dept. of Electrical and Computer Engineering, Rice University 6100 Main St., Houston, TX, 77005, USA