Schreck, Tobias ; Bernard, Jürgen ; Landesberger von Antburg, Tatiana ; Kohlhammer, Jörn (2009)
Visual Cluster Analysis of Trajectory Data With Interactive Kohonen Maps.
In: Information Visualization, 8 (1)
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Visual-interactive cluster analysis provides valuable tools for effectively analyzing large and complex data sets. Due to desirable properties and an inherent predisposition for visualization, the Kohonen Feature Map (or Self-Organizing Map, or SOM) algorithm is among the most popular and widely used visual clustering techniques. However, the unsupervised nature of the algorithm may be disadvantageous in certain applications. Depending on initialization and data characteristics, cluster maps (cluster layouts) may emerge that do not comply with user preferences, expectations, or the application context. Considering SOM-based analysis of trajectory data, we propose a comprehensive visual-interactive monitoring and control framework extending the basic SOM algorithm. The framework implements the general Visual Analytics idea to effectively combine automatic data analysis with human expert supervision. It provides simple, yet effective facilities for visually monitoring and interactively controlling the trajectory clustering process at arbitrary levels of detail. The approach allows the user to leverage existing domain knowledge and user preferences, arriving at improved cluster maps. We apply the framework on a trajectory clustering problem, demonstrating its potential in combining both unsupervised (machine) and supervised (human expert) processing, in producing appropriate cluster results.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2009 |
| Autor(en): | Schreck, Tobias ; Bernard, Jürgen ; Landesberger von Antburg, Tatiana ; Kohlhammer, Jörn |
| Art des Eintrags: | Bibliographie |
| Titel: | Visual Cluster Analysis of Trajectory Data With Interactive Kohonen Maps |
| Sprache: | Englisch |
| Publikationsjahr: | 2009 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Information Visualization |
| Jahrgang/Volume einer Zeitschrift: | 8 |
| (Heft-)Nummer: | 1 |
| Kurzbeschreibung (Abstract): | Visual-interactive cluster analysis provides valuable tools for effectively analyzing large and complex data sets. Due to desirable properties and an inherent predisposition for visualization, the Kohonen Feature Map (or Self-Organizing Map, or SOM) algorithm is among the most popular and widely used visual clustering techniques. However, the unsupervised nature of the algorithm may be disadvantageous in certain applications. Depending on initialization and data characteristics, cluster maps (cluster layouts) may emerge that do not comply with user preferences, expectations, or the application context. Considering SOM-based analysis of trajectory data, we propose a comprehensive visual-interactive monitoring and control framework extending the basic SOM algorithm. The framework implements the general Visual Analytics idea to effectively combine automatic data analysis with human expert supervision. It provides simple, yet effective facilities for visually monitoring and interactively controlling the trajectory clustering process at arbitrary levels of detail. The approach allows the user to leverage existing domain knowledge and user preferences, arriving at improved cluster maps. We apply the framework on a trajectory clustering problem, demonstrating its potential in combining both unsupervised (machine) and supervised (human expert) processing, in producing appropriate cluster results. |
| Freie Schlagworte: | Forschungsgruppe Visual Search and Analysis (VISA), Interactive information visualization, Visual analytics, Trajectory clustering, Self-organizing maps (SOM), Data exploration |
| 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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