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

摘要

We 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 and 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 flexble tool which can be used for various forms of explorative data analysis but it well also be made obivious that this flexibility comes with a price in terms of impaired performance.The usage of SOM in the data mining community is covered by discussing its application in the data mining tools CLEMENTINE and WEBSOM.