Knowledge Discovery in Databases

Ludwig-Maximilians-Universität München
Institut für Informatik
Lehr- und Forschungseinheit für Datenbanksysteme

University of Munich
Institute for Computer Science
Database and Information Systems

Knowledge Discovery in Databases

Knowledge discovery in databases (KDD) is the non-trivial extraction of implicit, previously unknown, and potentially useful information from databases.
Both, the number and the size of databases are rapidly growing because of the large amount of data obtained from satellite images, X-ray crystllography or other scientific equipment. This growth by far exceeds human capacities to analyze the databases in order to find implicit regularities, rules or clusters hidden in the data. Therefore, knowledge discovery becomes more and more important in databases. Typical tasks for KDD are the identification of classes (clustering), the prediction of new, unknown objects (classification), the discovery of associations or deviations in spatial databases. The term 'visual Data Mining' refers to the emphasis of integrating the user in the KDD process. Since these are challenging tasks, KDD algorithms should be incremental, i.e. when updating the database the algorithm does not have to be applied to the whole database.

Research Topics

Funding

Parts of this project are funded by the Deutsche Forschungsgemeinschaft (DFG). The German name of the project is "Clusterstruktur-Analyseverfahren und KDD-Basisoperationen zur effizienten, (semi-)automatischen Wissensextraktion in sehr grossen, hochdimensionalen Datenbanken" (KR 670/10-1).

Publications

List of Papers

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