The KDNet (= Knowledge Discovery Network of Excellence) focuses primarily on topics and disciplines related to knowledge discovery, machine learning and data mining.

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Knowledge Discovery

Knowledge Discovery refers to the overall process of generating patterns from data. The following steps are necessary for this:

• acquisition
• representation
• storage
• analysis
• visualization
• interpretation
• deployment

These steps are known as the "knowledge discovery cycle".

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Data Mining

Data Mining can be seen as the analytical step within this cycle. Rooting from three major research fields:

• data base theory
• statistics
• machine learning

Data Mining includes the practical study and evaluation of learning algorithms for different data formats, e.g. database tables, text documents, images, audio/video and spatial data.

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Related disciplines

The KDNet will contribute to unification of the different research fields relevant for Knowledge Discovery. The project results of European funded research projects will be integrated in the KDNet work.

Additionally, the KDNet interdisciplinary efforts will be directed towards establishing synergies to related disciplines, such as:

• bioinformatics and medicine
• economics
• (multimedia) information retrieval
• (multimedia) translation systems
• pattern recognition
• cognitive sciences
• Geographic Information Systems (GIS)

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