Machine learning, data mining and knowledge engineering

Machine learning and data mining aim at finding hidden patterns in arbitrary data. It contains knowledge discovery - like clusters, classifiers, association rules or rare events - in one-relational and multirelational data, spatio-temporal data, streams, various biomedical data as well as in linked data like web or social networks.

Main research areas

  • Relational data mining and inductive logic programming
  • Spatio-temporal data mining
  • Stream data mining
  • Data preprocessing
  • Educational data mining

Research team

Eva Mráková

PhD students:
Petr Glos (spatio-temporal data mining), Petr Kosina (stream mining, now in Porto), Jaroslav Bayer, Jan Géryk (mining in IS MU)

Jana Kadlecová (metalearning, planning to learn), Georg Schroeder (flood data analysis), Adam Šiška (logic), Jindřich Tandler (mining in social networks)

Knowledge Discovery Lab FI MU

Research in KD lab focuses on theory of inductive inference for learning from various data (multirelational, text, biomedical, streams) and to applications (spatio-temporal data, educational data). See lab pages for more information.

KD Lab tightly collaborates with INESC-LIAAD Porto and Universidade do Porto.


Luboš Popelínský
popel (somewhere at) fi [muni] cz
B418 FI MU