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Multi-Scale Visual Quality Assessment for Cluster Analysis with Self-Organizing Maps

Bernard, Jürgen ; Landesberger von Antburg, Tatiana ; Bremm, Sebastian ; Schreck, Tobias (2011)
Multi-Scale Visual Quality Assessment for Cluster Analysis with Self-Organizing Maps.
Visualization and Data Analysis 2011.
doi: 10.1117/12.872545
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (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.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2011
Autor(en): Bernard, Jürgen ; Landesberger von Antburg, Tatiana ; Bremm, Sebastian ; Schreck, Tobias
Art des Eintrags: Bibliographie
Titel: Multi-Scale Visual Quality Assessment for Cluster Analysis with Self-Organizing Maps
Sprache: Englisch
Publikationsjahr: 2011
Verlag: SPIE Press, Bellingham
Reihe: Proceedings of SPIE; 7868
Veranstaltungstitel: Visualization and Data Analysis 2011
DOI: 10.1117/12.872545
Kurzbeschreibung (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.

Freie Schlagworte: Forschungsgruppe Visual Search and Analysis (VISA), Cluster analysis, Self-organizing maps (SOM), Visualization quality, Visual analysis, Cluster comparison, Assessment
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Graphisch-Interaktive Systeme
Hinterlegungsdatum: 12 Nov 2018 11:16
Letzte Änderung: 22 Jul 2021 18:31
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