Visual Analytics methods to steer the subspace clustering process

• Antragsteller: Professor Dr. Oliver Deussen, seit 11/2012; Professor Dr. Thomas Seidl
• Fachliche Zuordnung: Sicherheit und Verlässlichkeit, Betriebs-, Kommunikations- und verteilte Systeme
• Förderung: Förderung von 2011 bis 2016
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 203034600

The main goal of this proposed project is the tight integration of visual analytics into the process of subspace cluster analysis to support the domain scientists’ exploration processes through a highly interactive immersive visualization. The considered databases from different fields of scientific and engineering research are usually very large and high dimensional. An approach solely based on automated subspace cluster analysis is rarely appropriate to provide the necessary insights into the various patterns, which are usually hidden by the huge amount and the heterogeneity of the data. Appropriate visualization techniques could not only help in monitoring the clustering process but, with special mining techniques, they also enable the domain expert to guide and even to steer the subspace clustering process to reveal the patterns of interest. To this goal we envision a concept that combines scalable subspace clustering algorithms and interactive scalable visual exploration techniques. This work will include the tasks of (1) comparative visualization and feedback guided computation of multiple alternative clusterings; (2) design of anytime subspace clustering algorithms, visualization of preliminary clustering results, intuitive annotation of these results and insertion of feedback into the algorithms; (3) methods for incremental adaptation of the analysis to data modifications. To the best of our knowledge the whole idea of using a visual analytics approach for steering clustering models is totally novel. The research we want to carry out in this project has the potential to open a new line of research that is not necessarily limited to subspace clustering but applies to any other modeling technique in which the involvement of end-users in the model building process can compensate for the limits the necessary heuristics introduce in the process.

• DFG-Verfahren: Schwerpunktprogramme
• Teilprojekt zu: SPP 1335: Scalable Visual Analytics: Interaktive visuelle Analysesysteme für komplexe Informationswelten
• Ehemaliger Antragsteller: Dr. Enrico Bertini, bis 11/2012