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Multiscale visual quality assessment for cluster analysis with self-organizing maps

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


Wong, P.C. ; Society for Imaging Science and Technology -IS&T-; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Visualization and Data Analysis 2011
Bellingham, WA: SPIE, 2011 (SPIE Proceedings 7868)
ISBN: 978-0-8194-8405-5
ISSN: 0277-786X
Paper 78680N, 12 S.
Conference on Visualization and Data Analysis (VDA) <11, 2011, San Francisco/Calif.>
Fraunhofer IGD ()
cluster analysis; self-organizing Maps (SOM); visualization quality; visual analysis; Forschungsgruppe Visual Search and Analysis (VISA)

Cluster analysis is an important data mining technique for analyzing large amounts of data, reducing many objects to a limited number of clusters. Cluster visualization techniques aim at supporting the user in better understanding the characteristics and relationships among the found clusters. While promising approaches to visual cluster analysis already exist, these usually fall short of incorporating the quality of the obtained clustering results. However, due to the nature of the clustering process, quality plays an important aspect, as for most practical data sets, typically many different clusterings are possible. Being aware of clustering quality is important to judge the expressiveness of a given cluster visualization, or to adjust the clustering process with refined parameters, among others.
In this work, we present an encompassing suite of visual tools for quality assessment of an important visual cluster algorithm, namely, the Self-Organizing Map (SOM) technique. We define, measure, and visualize the notion of SOM cluster quality along a hierarchy of cluster abstractions. The quality abstractions range from simple scalar-valued quality scores up to the structural comparison of a given SOM clustering with output of additional supportive clustering methods. The suite of methods allows the user to assess the SOM quality on the appropriate abstraction level, and arrive at improved clustering results. We implement our tools in an integrated system, apply it on experimental data sets, and show its applicability.