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Cluster correspondence views for enhanced analysis of SOM displays

: Bernard, Jürgen; Landesberger, Tatiana von; Bremm, Sebastian; Schreck, Tobias


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE Symposium on Visual Analytics Science and Technology 2010. Proceedings : VAST 2010, October 24 - 28, Salt Lake City, Utah, USA
Piscataway: IEEE Computer Society, 2010
ISBN: 978-1-4244-9486-6
Symposium on Visual Analytics Science and Technology (VAST) <2010, Salt Lake City/Utah>
Conference Paper
Fraunhofer IGD ()
visual analytic; cluster analysis; self-organizing Maps (SOM); quality measurement; Forschungsgruppe Visual Search and Analysis (VISA)

The Self-Organizing Map (SOM) algorithm is a popular and widely used cluster algorithm. Its constraint to organize clusters on a grid structure makes it very amenable to visualization. On the other hand, the grid constraint may lead to reduced cluster accuracy and reliability, compared to other clustering methods not implementing this restriction. We propose a visual cluster analysis system that allows to validate the output of the SOM algorithm by comparison with alternative clustering methods. Specifically, visual mappings overlaying alternative clustering results onto the SOM are proposed. We apply our system on an example data set, and outline main analytical use cases.