Institut für Informatik
Lehr- und Forschungseinheit für Datenbanksysteme
University of Munich
Institute for Computer Science
Database and Information Systems
Fully Automatic Data Mining (FADM)In most existing cases, DM is done completely algorithmic. Our research group explores different algorithmic approaches to similarity search for the sake of appropriate feedback for the user. Typically, similarity search is an iterative process and the visualization task is to find an intuitive representation of the similarity model such that it provides an explanation component for the retrieved objects as well as a query component based on the refinement of the last query.
Semi-Automatic Data Mining (SADM)This project divides DM into two parts, one of which is performed algorithmic, the other one is interactive. Chaining up both parts can yield a large gain in effectivity and efficiency in comparison to completely algorithmic approaches. With OPTICS, a new approach to cluster analysis has been developed which identifies the density-based clustering structure automatically. The ordering of the objects obtained by the algorithm is used for the visualization as an appropriate arrangement of the objects.
Interactive Data Mining (IDM)Our PBC system (Perception-Based Classification) maps the problem of finding an accurate decision tree on a visualization technique with interaction capabilities. This approach entails several advantages over existing algorithmic approaches. PBC enables backtracking during the tree construction and an arbitrary amount of split points. Since the decision tree is constructed by the user utilizing his perception, (domain) knowledge is transfered in both directions.
For access to the Web-based version of PBC, please contact Mihael Ankerst.