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

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

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BERNARD, Jürgen, Tatiana von LANDESBERGER, Sebastian BREMM, Tobias SCHRECK, 2011. Multiscale visual quality assessment for cluster analysis with self-organizing maps. IS&T/SPIE Electronic Imaging. San Francisco, California. In: WONG, Pak Chung, ed. and others. Visualization and Data Analysis 2011. IS&T/SPIE Electronic Imaging. San Francisco, California. SPIE, pp. 78680N-78680N-12. Available under: doi: 10.1117/12.872545

@inproceedings{Bernard2011-01-24Multi-16618, title={Multiscale visual quality assessment for cluster analysis with self-organizing maps}, year={2011}, doi={10.1117/12.872545}, number={7868}, publisher={SPIE}, series={SPIE Proceedings}, booktitle={Visualization and Data Analysis 2011}, pages={78680N--78680N-12}, editor={Wong, Pak Chung}, author={Bernard, Jürgen and Landesberger, Tatiana von and Bremm, Sebastian and Schreck, Tobias} }

Bremm, Sebastian Bernard, Jürgen Multiscale visual quality assessment for cluster analysis with self-organizing maps First publ. in: Visualization and data analysis 2011 : 24 - 25 January 2011, California, United States ; [part of] IS&T/SPIE electronic imaging, science and technology / sponsored and publ. by IS&T - the Society for Imaging Science and Technology; SPIE. Pak Chung Wong ... (Eds.). - Bellingham, Wash. : SPIE [u.a.], 2011. - pp. 7868 0N. - (Proceedings of SPIE ; 7868). - ISBN 978-0-8194-8405-5 eng 2011-11-08T11:13:05Z Schreck, Tobias Landesberger, Tatiana von 2011-01-24 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 di erent 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 re ned parameters, among others. In this work, we present an encompassing suite of visual tools for quality assessment of an important visual<br />cluster algorithm, namely, the Self-Organizing Map (SOM) technique. We de ne, 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. Landesberger, Tatiana von Schreck, Tobias Bernard, Jürgen Bremm, Sebastian deposit-license 2012-01-31T23:25:15Z

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