A remark to the GUHA method in the three-valued logic. - Jan Rauch (Kybernetika, 1975)

Main Problems and Further Possibilities of the Computer Realization of GUHA Procedures. - Jan Rauch (Int. J. Man Mach. Stud., 1981)

GUHA as data mining tool. - Jan Rauch (PAKM, 1996)

Logical Calculi for Knowledge Discovery in Databases. - Jan Rauch (PKDD, 1997)

Four-Fold Table Calculi for Discovery Science. - Jan Rauch (Discovery Science, 1998)

Classes of Four-Fold Table Quantifiers. - Jan Rauch (PKDD, 1998)

Deduction in Logic of Association Rules. - Jan Rauch (ASIAN, 1999)

Logics and Statistics for Association Rules and Beyond Abstract of Tutorial. - Petr Hájek 001; Jan Rauch (PKDD, 1999)

Mining for 4ft Association Rules. - Jan Rauch; Milan Simunek (Discovery Science, 2000)

Mining for Association Rules by 4ft-Miner. - Jan Rauch; Milan Simunek (INAP, 2001)

Mining and Querying in Association Rule Discovery. - Václav Lín; Jan Rauch; Vojtech Svátek (KDID, 2002)

Content-based Retrieval of Analytical Reports. - Václav Lín; Jan Rauch; Vojtech Svátek (RuleML, 2002)

The GUHA Method and Foundations of (Relational) Data Mining. - Petr Hájek 001; Martin Holena; Jan Rauch (Theory and Applications of Relational Structures as Knowledge Instruments, 2003)

Converting Association Rules into Natural Language - an Attempt. - Petr Strossa; Jan Rauch (IIS, 2003)

Analysis of guideline compliance - a data mining approach. - Vojtech Svátek; Antonín Ríha; Jan Peleska 002; Jan Rauch (CGP, 2004)

The GUHA Method, Data Preprocessing and Mining. - Petr Hájek 001; Jan Rauch; David Coufal; Thomas Feglar (Database Support for Data Mining Applications, 2004)

SDS-rules and association rules. - Tomás Karban; Jan Rauch; Milan Simunek (SAC, 2004)

Ontology-Enhanced Association Mining. - Vojtech Svátek; Jan Rauch; Martin Ralbovský (EWMF/KDO, 2005)

GUHA method and granular computing. - Jan Rauch; Milan Simunek (GrC, 2005)

Logic of Association Rules. - Jan Rauch (Appl. Intell., 2005)

An Alternative Approach to Mining Association Rules. - Jan Rauch; Milan Simunek (Foundations of Data Mining and knowledge Discovery, 2005)

Reporting Data Mining Results in a Natural Language. - Petr Strossa; Zdenek Cerný; Jan Rauch (Foundations of Data Mining and knowledge Discovery, 2005)

On the Logic of Medical Decision Support. - Patrik Eklund; Johan Karlsson 001; Jan Rauch; Milan Simunek (Theory and Applications of Relational Structures as Knowledge Instruments, 2006)

Many Sorted Observational Calculi for Multi-Relational Data Mining. - Jan Rauch (ICDM Workshops, 2006)

Definability of Association Rules in Predicate Calculus. - Jan Rauch (Foundations and Novel Approaches in Data Mining, 2006)

Mining for Patterns Based on Contingency Tables by KL-Miner - First Experience. - Jan Rauch; Milan Simunek; Václav Lín (Foundations and Novel Approaches in Data Mining, 2006)

Observational Calculi, Classes of Association Rules and F-property. - Jan Rauch (GrC, 2007)

Project SEWEBAR Considerations on Semantic Web and Data Mining. - Jan Rauch (IICAI, 2007)

Semantic Web Presentation of Analytical Reports from Data Mining - Preliminary Considerations. - Jan Rauch; Milan Simunek (Web Intelligence, 2007)

Lessons Learned from the ECML/PKDD Discovery Challenge on the Atherosclerosis Risk Factors Data. - Petr Berka; Jan Rauch; Marie Tomecková (Comput. Informatics, 2007)

Observational Calculi - Tool for Semantic Web. - Jan Rauch (ESWC (Posters), 2008)

Remarks to Logical Aspects of Measures of Interestingness of Association Rules. - Jan Rauch (ICDM Workshops, 2008)

LAREDAM - Considerations on System of Local Analytical Reports from Data Mining. - Jan Rauch; Milan Simunek (ISMIS, 2008)

Classes of Association Rules: An Overview. - Jan Rauch (Data Mining: Foundations and Practice, 2008)

Action Rules and the GUHA Method: Preliminary Considerations and Results. - Jan Rauch; Milan Simunek (ISMIS, 2009)

Dealing with Background Knowledge in the SEWEBAR Project. - Jan Rauch; Milan Simunek (Knowledge Discovery Enhanced with Semantic and Social Information, 2009)

Modifying Logic of Discovery for Dealing with Domain Knowledge in Data Mining. - Jan Rauch (CLA, 2010)

Meta-learning for Post-processing of Association Rules. - Petr Berka; Jan Rauch (DaWak, 2010)

Logic of Discovery, Data Mining and Semantic Web - Position Paper. - Jan Rauch (KDIR, 2010)

Mining and Post-processing of Association Rules in the Atherosclerosis Risk Domain. - Petr Berka; Jan Rauch (ITBAM, 2010)

SEWEBAR-CMS: A System for Postprocessing Data Mining Models. - Tomás Kliegr; David Chudán; Andrej Hazucha; Jan Rauch (RuleML Challenge, 2010)

An XML Format for Association Rule Models Based on the GUHA Method. - Tomás Kliegr; Jan Rauch (RuleML, 2010)

The GUHA method and its meaning for data mining. - Petr Hájek 001; Martin Holena; Jan Rauch (J. Comput. Syst. Sci., 2010)

Logical Aspects of the Measures of Interestingness of Association Rules. - Jan Rauch (Advances in Machine Learning II, 2010)

Consideration on a formal frame for data mining. - Jan Rauch (GrC, 2011)

Applying Domain Knowledge in Association Rules Mining Process - First Experience. - Jan Rauch; Milan Simunek (ISMIS, 2011)

Dealing with domain knowledge in association rules mining - Several experiments. - Jan Rauch; Milan Simunek (ICDKE, 2011)

Special issue on data analysis methodologies for intelligent systems: guest editor's introduction. - Tapio Elomaa; Petr Berka; Jan Rauch (J. Intell. Inf. Syst., 2011)

EuroMISE Center: Research and Education in Biomedical and Healthcare Informatics. - Jana Zvárová; Stepán Svacina; Jan Rauch; Jirí Haase; Hana Grünfeldová (EFMI-STC, 2012)

Formalizing data mining with association rules. - Jan Rauch (GrC, 2012)

Domain Knowledge and Data Mining with Association Rules - A Logical Point of View. - Jan Rauch (ISMIS, 2012)

EverMiner: consideration on knowledge driven permanent data mining process. - Jan Rauch (Int. J. Data Min. Model. Manag., 2012)

Using Domain Knowledge in Association Rules Mining - Case Study. - Jan Rauch; Milan Simunek (KDIR/KMIS, 2013)

Observational Calculi and Association Rules - Jan Rauch (Springer, 2013)

EverMiner Prototype Using LISp-Miner Control Language. - Milan Simunek; Jan Rauch (ISMIS, 2014)

Learning Association Rules from Data through Domain Knowledge and Automation. - Jan Rauch; Milan Simunek (RuleML, 2014)

Data Mining with Histograms - A Case Study. - Jan Rauch; Milan Simunek (ISMIS, 2015)

Formal Framework for Data Mining with Association Rules and Domain Knowledge - Overview of an Approach. - Jan Rauch (Fundam. Informaticae, 2015)

Logical Aspects of Dealing with Domain Knowledge in Data Mining with Association Rules. - Jan Rauch (Fundam. Informaticae, 2016)

Apriori and GUHA - Comparing two approaches to data mining with association rules. - Jan Rauch; Milan Simunek (Intell. Data Anal., 2017)

Data Mining with Histograms and Domain Knowledge - Case Studies and Considerations. - Jan Rauch; Milan Simunek (Fundam. Informaticae, 2019)

Expert deduction rules in data mining with association rules: a case study. - Jan Rauch (Knowl. Inf. Syst., 2019)

Enhanced Association Rules and Python. - Petr Mása; Jan Rauch (LOD (2), 2022)

A novel algorithm weighting different importance of classes in enhanced association rules. - Petr Mása; Jan Rauch (Knowl. Based Syst., 2024)

A novel algorithm for mining couples of enhanced association rules based on the number of output couples and its application. - Petr Mása; Jan Rauch (J. Intell. Inf. Syst., 2024)

Histograms as tools for mining actionable knowledge - principles and examples. - Jan Rauch (J. Intell. Inf. Syst., 2025)