Research group: Data Mining and Knowledge Discovery (DMKD)

DMKD’s color among the KIZI groups is blue, referring to the ‘color’ of the ‘oceans’ of data that can be submitted to data mining and knowledge discovery tools.


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Research focus

The Data Mining and Knowledge Discovery (DMKD) group at KIZI (one of its four research groups, overarched by the virtual Knowledge Engineering Group) undertakes research in analyzing various kinds of data in structured, semi-structured and textual form, and deriving useful knowledge from it. The focal areas of the group currently are:

The research on data mining had been present at the Department long before this term became coined: tools for combinatorial data analysis (KAD) and “learning an expert system from observational data” (ESOD, later re-implemented by P. Berka as KEX), both derived from the even earlier GUHA method, appeared, under supervision of J. Ivánek, in early 1980s. Since mid 1990s the flagship datamining tool of KIZI has been the LISp-Miner system (conceived by J. Rauch and developed by M. Šimůnek), currently after a major redesign centered around the new LM Workspace module and with scripting support based on the LISp-Miner Control Language (LMCL). Most recently, a family of web-oriented data mining tools arose under the leadership of T. Kliegr, such as (in 2011) integrating the CMS-based reporting tool SEWEBAR, leveraging on background knowledge.

In parallel there has been ongoing work on mining from texts, with special focus on Wikipedia: the Targeted Hypernym Discovery method (THD, now part of the tool) and the associated LHD dataset.

The research has been supported by a number of research projects. The most important had recently been LinkedTV, an Integrated Project funded by the EU FP7 (2011-2015), under which the text mining tool and the recommender had been developed (under the supervision of T. Kliegr). There had also been several CSF (Czech Science Foundation) projects, coordinated by J. Rauch. More recently, the group had been engaged in the EU Horizon 2020 project (2015-2017), where analyses of fiscal data using various EasyMiner components took place.

The group also co-organized several international events, most notably, RuleML 2014  (T. Kliegr, J. Rauch) and ISMIS 2009 (J. Rauch, P. Berka), several editions of the ECML/PKDD Discovery Challenge (P. Berka) and of the Linked Data Mining Challenge (V. Svátek).


Group leaders: Jan Rauch, Tomáš Kliegr, Petr Berka

Other group members:

Past members: Jan Bouchner, Barbora Červenková, Milan Dojchinovski, Přemysl Václav Duben, Ivo Lašek, Andrej Hazucha, Linda Horáková, Martin Labský, Bohuslav Koukal, Jan Nemrava, David Pejčoch, Kamil Soule, Radek Škrabal.


Within the University, the DMKD group mainly cooperates with

Within the Czech Republic, there has been lasting cooperation with the Web Intelligence group (led by Dr. Tomáš Vitvar) at the Czech Technical University. In particular, PhD students from the CTU group (M. Dojchinovski, J. Kuchař and I. Lašek) had been directly involved in research activities of the LinkedTV project, and J. Kuchař also in the project.

At the international level, the group collaborates with numerous foreign partners, either within EU projects (in particularly, the EU FP7 IP LinkedTV project) or on informal basis. Examples of such joint research are:

Selected recent publications


Activities of the group are reflected in several courses taught at the University, most notably the MSc level courses:

A specialized Bc level course is:

A data mining primer is also provided as part of the Bc level course (mandatory for all students of the Applied Informatics specialty):

Finally, there are also two relevant PhD-level courses:

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