Current events




More... »

Advanced search

Research group: Data Mining and Knowledge Discovery (DMKD)

Related pages

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.


  • 9 March 2018: An article on the EasyMiner system (see also the system webpage), by S. Vojíř and colleagues, appeared online in the Elsevier’s KnowSys journal (IF > 4.5).
  • 12 February 2018: The first term of the brand-new MSc.-level course on Data Science in Python and R (taught by T. Kliegr) started. The department’s curriculum is now better balanced wrt. the modern programming-oriented paradigms in data science.
  • 13 November 2017: In the prestigious ACM-endorsed Czecho-Slovak competition of MSc. theses, IT SPY (the base round involving about 1900 theses from 20 universities), that by B. Koukal, supervised by D. Chudán, had been invited to the final round (of best nine theses) and there it obtained the SAP Award for Contribution to the Field of Enterprise Information Systems. Big congratulations!
  • 1 November 2017: An article on the InBeat system, by J. Kuchař and T. Kliegr, appeared in the Elsevier’s KnowSys journal (IF > 4.5).
  • October 2017: A new PhD student, Ivan Jelínek, joined the group (being already in his 3rd year, by transfer from DIT due to his advisor’s retirement). His topic is “Unstructured Data Analysis on Social Networks”; he will be supervised by P. Strossa from SWOE (officially and along the linguistic line) but presumably also helped by relevant DMKD folks.
  • 19 August 2017: An article comparing the GUHA and Apriori data mining methods, by J. Rauch and M. Šimůnek, appeared in the IOS Press’ IDA journal.
  • 21 June 2017: The LHD tool by T. Kliegr won the 1st round of the DBpedia Open Extraction Challenge (TextExt) collocated with the LDK conference.
  • 31 November 2016: The JWS journal paper by T. Kliegr, joint with  O. Zamazal (SWOE group) was ranked 3rd in the annual Rector’s award. Congratulations!
  • October, 2016: The project “Pilot application for distributed analysis of big data” (led by T. Kliegr), funded by the CESNET association, has been successfully defended.
  • October 4, 2016: Tomáš Kliegr gave a talk on association rule classification and the EasyMiner system developed by the DMKD group at the IEEE Days at the University of West Bohemia.
  • October 2016: A new PhD student, Jiří Zettel, joined the group. He will be supervised by prof. Petr Berka and his topic will be related to data pre-processing for data mining.
  • September 20, 2016: Stanislav Vojíř has successfuly defended his PhD thesis “Business Rule Learning using data mining of GUHA association rules”. He remains member of our team.
  • August 2016: The LHD dataset developed by our group is available for download as part of the DBpedia 2015 release.
  • June 2016: A new paper by Tomáš Kliegr  (and Ondřej Zamazal): LHD 2.0: A text mining approach to typing entities in knowledge graphs appears in Elsevier’s Journal of Web Semantics. It follows up with the recent  LHD paper in the same journal.
  • April 2016: T. Kliegr and his team presented EasyMiner at the popular Machine Learning Meetup.

(For older news see page bottom)

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:

  • Developing new tools for dealing with domain knowledge in data mining
  • Developing new tools for automation of data mining
  • Accessing “classical” data mining tools via a web interface, using a “web search” metaphor, and sharing data mining results in structured form over the web
  • Extracting structured data from free or semi-structured text
  • Disambiguation of textual entities, their open-class classification and linking to semantic resources (such as DBpedia)
  • Web / multimedia usage mining and user preference learning.

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:

  • Faculty: David Chudán, Jiří Ivánek, Vojtěch Svátek, Milan Šimůnek, Stanislav Vojíř
  • Project worker: Jaroslav Kuchař (primarily at CTU, Prague)
  • PhD students: Ivan Jelínek, Viktor Nekvapil, Václav Zeman, Jiří Zettel
  • MSc students: Jakub Kúdela, Kamil Soule

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, Radek Škrabal.


Within the University, the DMKD group mainly cooperates with

  • The Semantic Web and Ontological Engineering (SWOE) group within the same department. In particular, SWOE promotes the achievements of DMKD, such as the Linked Hypernym Dataset (LHD), in the semantic web and Linked Data community. There is also joint research in the field of background knowledge for text mining (e.g. the Ex information extractor project).
  • The business intelligence group (led by Dr. Ota Novotný) at the neighboring Dept. of Information Technology (KIT). The overlaping interest is in applying data mining techniques on the top of OLAP-powered data warehouses. In 2014-2016 three DMKD members directly cooperated on KIT’s BI-oriented project funded by TACR, the Technological Agency of the Czech Republic.

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:

  • Linked data mining, with University of Mannheim and University of Darmstadt
  • User interest mining, with University of Mons, Belgium (Numediart institute)
  • Action rules mining, with University of North Carolina, Charlotte, US
  • Logical calculi for data mining, with Technical University of Tampere, Finland

Selected recent publications

    • Vojíř S., Zeman V., Kuchař J., Kliegr T.: Web Framework for Interpretable Machine Learning based on Rules and Frequent Itemsets. Knowledge-Based Systems, In Press, March 2018.
    • Kuchař J., Kliegr T.: InBeat: JavaScript recommender system supporting sensor input and linked data. Knowledge-Based Systems, Volume 135, November 2017, 40–43.
    • Rauch J., Šimůnek M.: Apriori and GUHA – Comparing two approaches to data mining with association rules. Intelligent Data Analysis, vol. 21, no. 4, pp. 981-1013, 2017.
    • Vojíř S., Smutný Z.: Business Rules Mining Using GUHA Method for the Personalization of Commercial Offers. Engineering Economics, Vol 28, No 2 (2017).
    • Kliegr T., Zamazal O.: LHD 2.0: A text mining approach to typing entities in knowledge graphs. J. Web Semantics, Volume 39, August 2016, 47-61.
    • Kliegr T.: Linked hypernyms: Enriching DBpedia with Targeted Hypernym Discovery. J. Web Semantics, Volume 31, March 2015, 59-69, 2015
    • Rauch J., Šimůnek M.: Data Mining with Histograms – A Case Study. In: ISMIS 2015. Springer, LNCS.
    • Rauch J.: Formal Framework for Data Mining with Association Rules and Domain Knowledge – Overview of an Approach. Fundamenta Informaticae, 2015, Vol. 137, No. 2, 171–217.
    • Rauch J., Šimůnek M.: Dobývání znalostí z databází, LISp-Miner a GUHA. Oeconomica, 2014. 462 pages. ISBN 978-80-245-2033-9.
    • Fürnkranz J., Kliegr T.: A Brief Overview of Rule Learning. In: RuleML 2015: 54-69.
    • Rauch J., Šimůnek M.: Learning Association Rules from Data through Domain Knowledge and Automation. In: Rules on the Web (RuleML 2014). Springer LNCS, 2014, .
    • Šimůnek M., Rauch J.: EverMiner Prototype Using LISp-Miner Control Language. In: Foundations of Intelligent Systems (ISMIS 2014). Springer LNCS.
    • Šimůnek M.: LISp-Miner Control Language description of scripting language implementation. Journal of systems integration, 2014, Vol. 5, No. 2, online.
    • Rauch J.: Observational Calculi and Association Rules. Studies in Computational Intelligence, Vol. 469, Springer, 2013.
    • Kuchař J., Kliegr T.: GAIN: web service for user tracking and preference learning – a smart TV use case. In: RecSys ’13, ACM, 2013.
    • Chudán D., Svátek V.: Advanced Mining of Association Rules over Periodic Snapshots in a Data Warehouse. In: I-KNOW 2013, ACM, 28:1-28:4, 2013
    • Berka P.: Towards Comprehensive Concept Description Based on Association Rules. In: IDA’13, Springer LNCS, 2013.
    • Dojchinovski M., Kliegr T.: Real-Time Classification of Entities in Text with Wikipedia. In: ECML-PKDD’13, Springer LNCS, 2013.
    • Škrabal R., Šimůnek M., Vojíř S., Hazucha A., Marek T., Chudán D., Kliegr T.: Association Rule Mining Following the Web Search Paradigm. In: ECML-PKDD’12, Springer LNCS, 2012.
    • Berka P.: Learning compositional decision rules using the KEX algorithm. Intelligent Data Analysis, 2012, Vol. 16, No. 4.


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:

Older news:

  • November 2015: Jan Rauch and Milan Šimůnek have been awarded the UEP Rector’s Prize for their book on the  LISp-Miner system.
  • September 2015: David Chudán successfully defended his PhD thesis on Association rule mining as a support for OLAP on September 22. Congratulations! (David remains at the Department, now as project worker and manager funded from
  • September 2015: The LHD dataset developed by our group is available for download as part of the DBpedia 2015 release.
  • September 2015: Tomáš Kliegr is starting his 6-month post-doc internship at University of Darmstadt. He will be mainly working with prof. Johannes Fürnkranz in the field of rule/preference learning.
  • August 2015: At the RuleML 2015 conference in Berlin, the DMKD team presented the new EasyMiner/R interface, and Tomáš Kliegr co-chaired the RecSysRules 2015 challenge.
  • June 2015: Václav Zeman defended his PhD project progress on “Data mining on linked data” (after first year).
  • May 2015: The successful Know@LOD workshop with 3rd Linked Data Mining Challenge (LDMC), co-chaired by V. Svátek, was held at the ESWC2015 conference in Portoroz – it was the most attended of all 16 workshops.
  • May 2015: The EU Horizon 2020 project (web still under construction) started. The DMKD team (led by V. Svátek) will contribute to the WP related to budget/spending open data mining.
  • May 2015: The EU LinkedTV Integrated project was concluded by a successful final review. The rating of the project eventually was ‘Excellent Progress’ (i.e., the best possible). A decent part of the project outcomes is due to the DMKD group development efforts (coordinated by T. Kliegr).
  • February 2015: Tomáš Kliegr has been awarded a CESNET grant on “Pilot application for distributed analysis of big data”, which will allow his team to employ the computing capacity of the CESNET Metacenter for the research tasks addressed by the DMKD group.
  • February 2015: Stanislav Vojíř obtained the Best Paper award in the Applied Informatics category at the annual PhD research symposium of the Faculty.
  • February 2015: David Pejčoch succesfully defended his PhD thesis on “complex management of data and information quality”.
  • November 2014: The article “Linked Hypernyms: Enriching DBpedia with Targeted Hypernym Discovery”, by Tomáš Kliegr, has been accepted to the Elsevier Journal of Web Semantics (IF=1.377), see the article page
  • October 2014: The Linked Hypernym Dataset (LHD), a large collection of type assignments to RDF entities built by THD tool co-developed by Tomáš Kliegr, has been integrated into the official version of German DBpedia.
  • September 2014: A new PhD student, Václav Zeman, enrolled in September 2014 (with V. Svátek as Advisor) and joined the group (as well as the SWOE group). He will be working on novel data mining techniques (with special focus on linked data), as well as upkeeping the Czech DBpedia.
  • August 2014: A project named Automated business rules extraction with feedback loop, funded by TACR, Technological Agency of the Czech Republic, started. Jan Rauch, Milan Šimůnek and Stanislav Vojíř take part in this project.
  • August 2014: The RuleML conference collocated with ECAI 2014 (Aug 18-20, 2014) was co-organized by Jan Rauch,Tomáš Kliegr and Stanislav Vojíř as Local Chairs. Tomáš also co-organized its Special Track on ‘Learning (Business) Rules from Data’.
  • May 2014: The second edition of the Linked Data Mining Challenge was co-organized by Vojtěch Svátek in connection with the Know@LOD workshop (May 25, 2014) collocated with the ESWC conference in Crete.