Share Email Print
cover

Proceedings Paper

Multiscale visual quality assessment for cluster analysis with self-organizing maps
Author(s): Jürgen Bernard; Tatiana von Landesberger; Sebastian Bremm; Tobias Schreck
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

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.

Paper Details

Date Published: 24 January 2011
PDF: 12 pages
Proc. SPIE 7868, Visualization and Data Analysis 2011, 78680N (24 January 2011); doi: 10.1117/12.872545
Show Author Affiliations
Jürgen Bernard, Technische Univ. Darmstadt (Germany)
Tatiana von Landesberger, Technische Univ. Darmstadt (Germany)
Sebastian Bremm, Technische Univ. Darmstadt (Germany)
Tobias Schreck, Technische Univ. Darmstadt (Germany)


Published in SPIE Proceedings Vol. 7868:
Visualization and Data Analysis 2011
Pak Chung Wong; Jinah Park; Ming C. Hao; Chaomei Chen; Katy Börner; David L. Kao; Jonathan C. Roberts, Editor(s)

© SPIE. Terms of Use
Back to Top