SteerSCiVA: Steerable Subspace Clustering for Visual Analytics

Research topic: Data Analysis and Knowledge Extraction

The main goal of this 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 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.

This project "SteerSCiVA: Steerable Subspace Clustering for Visual Analytics" is funded by the DFG in the SPP "Scalable Visual Analytics" and is conducted in collaboration with the “Dep. of Computer and Information Science, University of Konstanz".