Automated matching of data mining dataset schemata to background knowledge. - Stanislav Vojír; Tomás Kliegr; Vojtech Svátek; Ondrej Sváb-Zamazal (OM, 2011)

Association Rule Mining Following the Web Search Paradigm. - Radek Skrabal; Milan Simunek; Stanislav Vojír; Andrej Hazucha; Tomás Marek; David Chudán; Tomás Kliegr (ECML/PKDD (2), 2012)

Transforming Association Rules to Business Rules: EasyMiner meets Drools. - Stanislav Vojír; Tomás Kliegr; Andrej Hazucha; Radek Skrabal; Milan Simunek (RuleML (2), 2013)

Learning Business Rules with Association Rule Classifiers. - Tomás Kliegr; Jaroslav Kuchar; Davide Sottara; Stanislav Vojír (RuleML, 2014)

Business Rule Learning with Interactive Selection of Association Rules. - Stanislav Vojír; Premysl Václav Duben; Tomás Kliegr (Challenge+DC@RuleML, 2014)

Extension of Business Rule Sets Using Data Mining of GUHA Association Rules. - Stanislav Vojír (ITAT, 2015)

EasyMiner/R Preview: Towards a Web Interface for Association Rule Learning and Classification in R. - Stanislav Vojír; Vaclav Zeman; Jaroslav Kuchar; Tomás Kliegr (Challenge+DC@RuleML, 2015)

EasyMiner - Short History of Research and Current Development. - Tomás Kliegr; Jaroslav Kuchar; Stanislav Vojír; Václav Zeman (ITAT, 2017)

Using EasyMiner API for Financial Data Analysis in the OpenBudgets.eu Project. - Stanislav Vojír; Vaclav Zeman; Jaroslav Kuchar; Tomás Kliegr (RuleML+RR (Supplement), 2017)

Dolování z otevřených dat o rozpočtech a výdajích. - David Chudán; Vojtech Svátek; Jaroslav Kuchar; Stanislav Vojír (Acta Informatica Pragensia, 2018)

EasyMiner.eu: Web framework for interpretable machine learning based on rules and frequent itemsets. - Stanislav Vojír; Vaclav Zeman; Jaroslav Kuchar; Tomás Kliegr (Knowl. Based Syst., 2018)

Editable machine learning models? A rule-based framework for user studies of explainability. - Stanislav Vojír; Tomás Kliegr (Adv. Data Anal. Classif., 2020)