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 the project combines scalable subspace clustering algorithms and interactive scalable visual exploration techniques. The project includes 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.
