Main reference

• Vojíř, S., Zeman, V., Kuchař, J., & Kliegr, T. (2018). .: EasyMiner.eu: Web framework for interpretable machine learning based on rules and frequent itemsets.Knowledge-Based Systems, 150, 111-115., 2018, freely accessible preprint

Outlier (Anomaly) detection in EasyMiner

• Jaroslav Kuchař, Adam Ashenfelter,Tomáš Kliegr: Outlier (Anomaly) Detection Modelling in PMML. Rule Challenge@RuleML+RR2017. Vol-1875 of CEUR-WS, 2017
• Stanislav Vojíř, Václav Zeman, Jaroslav Kuchař, Tomás Kliegr: Using EasyMiner API for Financial Data Analysis in the OpenBudgets.eu Project. Rule Challenge@RuleML+RR2017. Vol-1875 of CEUR-WS, 2017

Representation of rule-based and frequent-itemset based models.

• Jaroslav Kuchař, Adam Ashenfelter,Tomáš Kliegr: Outlier (Anomaly) Detection Modelling in PMML. Rule Challenge@RuleML+RR2017. Vol-1875 of CEUR-WS, 2017
• Tomáš Kliegr, Jan Rauch: An XML Format for Association Rule Models Based on the GUHA Method. RuleML 2010: p. 273-288.
• Tomáš Kliegr, Jan Zemánek, and Marek Ovečka.  Topic Maps for Association Rule Mining. Topic Maps Research and Applications: TMRA 2009. Leipzig.

EasyMiner/R

• Stanislav Vojíř, Václav Zeman, Jaroslav Kuchař, Tomáš Kliegr: EasyMiner/R Preview: Towards a Web Interface for Association Rule Learning and Classification in R. RuleML 2015

Benchmarks

• Tomáš Kliegr, Jaroslav Kuchař: Benchmark of rule-based classifiers in the news recommendation task. CLEF 2015 Proceedings, p. 130–141.
• Jiří Filip, Tomáš Kliegr: Classification based on Associations (CBA)-a performance analysis. RuleML Challenge, 2018

EasyMiner/Cloud (Spark)

• Václav Zeman, Stanislav Vojíř, Jaroslav Kuchař, Tomáš Kliegr: Využití cloudu pro dolování asociačních pravidel
  z velkých dat přes webové rozhraní.  WIKT/DaZ 2016. Presentation

EasyMiner and Business Rules

• Tomáš Kliegr, Jaroslav Kuchař, Davide Sottara, Stanislav Vojíř: Learning Business Rules with Association Rule Classifiers. RuleML 2014
• Stanislav Vojíř, Přemysl Václav Duben, Tomáš Kliegr: Business Rule Learning with Interactive Selection of Association Rules. RuleML Challenge 2014
• Stanislav Vojíř, Tomáš Kliegr, Andrej Hazucha, Radek Škrabal, Milan Šimůnek: Transforming Association Rules to Business Rules: EasyMiner meets Drools. RuleML 2013.

EasyMiner and background (domain) knowledge

• Tomáš Kliegr, Andrej Hazucha, Tomáš Marek: Instant Feedback on Discovered Association Rules with PMML-Based Query-by-Example. RR 2011: 257-262.
• Tomáš Kliegr, Stanislav Vojíř, Jan Rauch. Background knowledge and PMML: first considerations. PMML '11 Proceedings of the 2011 workshop on Predictive markup language modeling.
• Stanislav Vojíř, Tomáš Kliegr, Vojtech Svátek, Ondřej Šváb-Zamazal: Automated matching of data mining dataset schemata to background knowledge. OM 2011.

LISp-Miner backend

• Jan Rauch, Milan Šimůnek: An Alternative Approach to Mining Association Rules. In Lin T Y, Ohsuga S, Liau C J, and Tsumoto S (eds): Data Mining: Foundations, Methods, and Applications, Springer-Verlag, 2005.
• Jan Rauch: Association Rules and Mechanizing Hypotheses Formation. Freiburg 03.09.2001. In: KORB, Kevi, BENSUSAN, Hilan (ed.). ECML/PKDD – 2001. Machine Learning as Experimental Philosophy of Science. Freiburg : University Freiburg, 2001. 17 s.
• Jan Rauch, Milan Šimůnek: Mining for 4ft Association Rules by 4ft-Miner. in: INAP 2001, The Proceeding of the International Conference On Applications of Prolog. Prolog Association of Japan, Tokyo October 2001, pp. 285–294.
• Milan Šimůnek: Academic KDD Project LISp-Miner. In: ABRAHAM, A., FRANKE, K., KOPPEN, K. (ed.). Advances in Soft Computing – Intelligent Systems Desing and Applications. Heidelberg: Springer-Verlag, 2003, pp. 263–272. ISBN 3-540-40426-0.

EasyMiner and Automated Report Generation ("SEWEBAR" project)

• Tomáš Kliegr, Vojtěch Svátek, Martin Ralbovský, Milan Šimůnek: SEWEBAR-CMS: semantic analytical report authoring for data mining results. Journal of Intelligent Information Systems. Springeer, 2010.
• Tomáš Kliegr, David Chudán, Andrej Hazucha, Jan Rauch. SEWEBAR-CMS: A System for Postprocessing Association Rule Models. In: RuleML-2010 Challenge; p. 1-8. CEURWS, 2010. ISSN: 1613-0073. Runner Up Prize
• Andrej Hazucha, Jakub Balhar, Tomáš Kliegr: A PHP library for Ontopia-CMS Integration. In: Information Wants to be a Topic Map. Leipzig, 2010. p. 177-182. ISBN: 978-3-941608-11-5.
• Andrej Hazucha, Tomáš Kliegr, Vojtěch Svátek: Importing Knowledge Fragments to CMS-Enabled Data Mining Analytical Reports. In: IRMLES Workshop at ESWC 2010. Crete.
• Tomáš Kliegr, Vojtěch Svátek, Milan Šimůnek, Daniel Šťastný, Andrej Hazucha: An XML Schema and a Topic Map Ontology for Formalization of Background Knowledge in Data Mining. In: IRMLES Workshop at ESWC 2010. Crete, 2010.

Other

• Tomáš Kliegr, Jaroslav Kuchař, Stanislav Vojíř, Václav Zeman : EasyMiner – Short History of Research and Current Development. ITAT 2017 Proceedings, pp. 235–239CEUR Workshop Proceedings Vol. 1885, ISSN 1613-0073.
• Václav Zeman. Analýza cloudového řešení akademického nástroje pro dolování pravidel z databází. Systémová integrace. 2016, roč. 23, č. 3–4, s. 56–73. ISSN 1210-9479. .