Vybrané publikace

• Vojíř S., Zeman V., Kuchař J., Kliegr T.: EasyMiner.eu: Web Framework for Interpretable Machine Learning based on Rules and Frequent Itemsets. Knowledge-Based Systems, Volume 150, 15 June 2018, 111-115.
• Kliegr T., Zamazal O.: Antonyms are similar: Towards paradigmatic association approach to rating similarity in SimLex-999 and WordSim-353. Knowledge-Based Systems, Volume 115, May 2018, 174-193
• Berka P.: Comprehensive concept description based on association rules: A meta-learning approach. Intelligent Data Analysis, vol. 22, no. 2, pp. 325-344, 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.: Entityclassifier.eu: 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.