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OFAI-TR-99-04 ( 42kB g-zipped PostScript file,  263kB PDF file)

On the use of self-organizing maps for clustering and visualization

Arthur Flexer

We will show that the number of output units used in a self-organizing map (SOM) influences its applicability for either clustering or visualization. By reviewing the appropriate literature and theory as well as our own empirical results, we demonstrate that SOMs can be used for clustering or visualization separately, for simultaneous clustering and visualization, and even for clustering via visualization. For all these different kinds of application, SOM is compared to other statistical approaches. This will show SOM to be a very flexible tool which can be used for various forms of explorative data analysis but it will also be made obvious that this flexibility comes with a price in terms of impaired performance.

Keywords: Neural Networks, Self-Organizing Maps, Clustering, Visualization, Multidimensional Scaling, Statistics

Citation: Flexer A.: On the use of self-organizing maps for clustering and visualization, in Zytkow J.M. and Rauch J.(eds.), Principles of Data Mining and Knowledge Discovery, Third European Conference, PKDD'99, Prague, Czech Republic, Proceedings, Lecture Notes in Artificial Intelligence 1704, Springer, p.80-88, 1999.