Increasingly large data resources require automatic techniques for gaining knowledge which means extracting interesting, unknown patterns from the data. In scenarios with many attributes or with noise, these patterns are typically hidden in subspaces of the data and do not show up in the full dimensional space; therefore, we develop new data mining techniques including subspace clustering or outlier detection. For data streams which constitute endless data resources, we develop specialized algorithms that can handle both the infinite amount of data and the limited and often varying amount of time available between two stream data items. Typical tasks are clustering of streaming data or classification on data streams.

• UP-BEAT
• SteerSCiVA: Steerable Subspace Clustering for Visual Analytics
• SFB 686
• MOA - Massive Online Analysis
• Anytime Stream Mining
• Research cluster UMIC
• Subspace Mining for High Dimensional Data
• Outlier Ranking for High Dimensional Data
• OpenSubspace Framework
• Clustering in Attributed Graphs
• NISIS - Nature Inspired Intelligent Systems