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Data mining, machine learning, and knowledge-based modelling

In our department data mining, machine learning and knowledge-based modelling techniques serve as important methodologies for solving mathematical problems in different application scenarios within EU-projects (DynaVis, SynTex) and industrial projects. Typical such application comprise:

  • Fault detection in quality control systems
  • Image classification and segmentation
  • Prediction and decision support in complex systems
  • Grouping of data and knowledge discovery

We apply these methodologies to data from different types of sources, namely measurement data and signal streams as well as image data. The applied methodologies can loosely be divided into:

  • Regression (supervised): system identification, approximation and prediction
  • Pattern recognition and classification (supervised): decision support for operators, fault detection
  • Clustering (unsupervised): grouping, image segmentation, data compression
  • Feature selection (un- or supervised): elimination of superfluous information
  • Knowledge-based modelling with fuzzy systems: discovery of interpretable models from data, coding of expert knowledge

New developments and publications are conducted in the fields of evolving fuzzy systems, incremental learning, clustering and fault detection. Hereby, a special emphasis is set on on-line learning methods, which are able to refine and improve models during on-line production in real-time.