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2020 – today
- 2024
[j14]Petr Mása
, Jan Rauch:
A novel algorithm for mining couples of enhanced association rules based on the number of output couples and its application. J. Intell. Inf. Syst. 62(2): 431-458 (2024)- [j13]Petr Mása, Jan Rauch:
A novel algorithm weighting different importance of classes in enhanced association rules. Knowl. Based Syst. 294: 111741 (2024) - 2022
- [c40]Petr Mása, Jan Rauch:
Enhanced Association Rules and Python. LOD (2) 2022: 123-138
2010 – 2019
- 2019
- [j12]Jan Rauch, Milan Simunek:
Data Mining with Histograms and Domain Knowledge - Case Studies and Considerations. Fundam. Informaticae 166(4): 349-378 (2019) - [j11]Jan Rauch:
Expert deduction rules in data mining with association rules: a case study. Knowl. Inf. Syst. 59(1): 167-195 (2019) - 2017
- [j10]Jan Rauch, Milan Simunek:
Apriori and GUHA - Comparing two approaches to data mining with association rules. Intell. Data Anal. 21(4): 981-1013 (2017) - 2016
- [j9]Jan Rauch:
Logical Aspects of Dealing with Domain Knowledge in Data Mining with Association Rules. Fundam. Informaticae 148(1-2): 1-33 (2016) - 2015
- [j8]Jan Rauch:
Formal Framework for Data Mining with Association Rules and Domain Knowledge - Overview of an Approach. Fundam. Informaticae 137(2): 171-217 (2015) - [c39]Jan Rauch, Milan Simunek:
Data Mining with Histograms - A Case Study. ISMIS 2015: 3-8 - 2014
- [c38]Milan Simunek, Jan Rauch:
EverMiner Prototype Using LISp-Miner Control Language. ISMIS 2014: 113-122 - [c37]Jan Rauch, Milan Simunek:
Learning Association Rules from Data through Domain Knowledge and Automation. RuleML 2014: 266-280 - 2013
- [b1]Jan Rauch:
Observational Calculi and Association Rules. Studies in Computational Intelligence 469, Springer 2013, ISBN 978-3-642-11736-7, pp. 1-282 - [c36]Jan Rauch, Milan Simunek:
Using Domain Knowledge in Association Rules Mining - Case Study. KDIR/KMIS 2013: 104-111 - 2012
- [j7]Jan Rauch:
EverMiner: consideration on knowledge driven permanent data mining process. Int. J. Data Min. Model. Manag. 4(3): 224-243 (2012) - [c35]Jana Zvárová, Stepán Svacina, Jan Rauch, Jirí Haase, Hana Grünfeldová:
EuroMISE Center: Research and Education in Biomedical and Healthcare Informatics. EFMI-STC 2012: 53-56 - [c34]Jan Rauch:
Formalizing data mining with association rules. GrC 2012: 406-411 - [c33]Jan Rauch:
Domain Knowledge and Data Mining with Association Rules - A Logical Point of View. ISMIS 2012: 11-20 - 2011
- [j6]Tapio Elomaa, Petr Berka, Jan Rauch:
Special issue on data analysis methodologies for intelligent systems: guest editor's introduction. J. Intell. Inf. Syst. 37(3): 291-292 (2011) - [c32]Jan Rauch:
Consideration on a formal frame for data mining. GrC 2011: 562-569 - [c31]Jan Rauch, Milan Simunek:
Applying Domain Knowledge in Association Rules Mining Process - First Experience. ISMIS 2011: 113-122 - [c30]Jan Rauch, Milan Simunek:
Dealing with domain knowledge in association rules mining - Several experiments. ICDKE 2011: 13-17 - 2010
- [j5]Petr Hájek, Martin Holena, Jan Rauch:
The GUHA method and its meaning for data mining. J. Comput. Syst. Sci. 76(1): 34-48 (2010) - [c29]Jan Rauch:
Modifying Logic of Discovery for Dealing with Domain Knowledge in Data Mining. CLA 2010: 175-186 - [c28]Petr Berka, Jan Rauch:
Meta-learning for Post-processing of Association Rules. DaWak 2010: 251-262 - [c27]Jan Rauch:
Logic of Discovery, Data Mining and Semantic Web - Position Paper. KDIR 2010: 342-351 - [c26]Petr Berka, Jan Rauch:
Mining and Post-processing of Association Rules in the Atherosclerosis Risk Domain. ITBAM 2010: 110-117 - [c25]Tomás Kliegr, David Chudán, Andrej Hazucha, Jan Rauch:
SEWEBAR-CMS: A System for Postprocessing Data Mining Models. RuleML Challenge 2010 - [c24]Tomás Kliegr, Jan Rauch:
An XML Format for Association Rule Models Based on the GUHA Method. RuleML 2010: 273-288 - [p10]Jan Rauch:
Logical Aspects of the Measures of Interestingness of Association Rules. Advances in Machine Learning II 2010: 175-203
2000 – 2009
- 2009
- [c23]Jan Rauch, Milan Simunek:
Action Rules and the GUHA Method: Preliminary Considerations and Results. ISMIS 2009: 76-87 - [p9]Jan Rauch, Milan Simunek:
Dealing with Background Knowledge in the SEWEBAR Project. Knowledge Discovery Enhanced with Semantic and Social Information 2009: 89-106 - [e2]Jan Rauch, Zbigniew W. Ras, Petr Berka, Tapio Elomaa:
Foundations of Intelligent Systems, 18th International Symposium, ISMIS 2009, Prague, Czech Republic, September 14-17, 2009. Proceedings. Lecture Notes in Computer Science 5722, Springer 2009, ISBN 978-3-642-04124-2 [contents] - 2008
- [c22]Jan Rauch:
Observational Calculi - Tool for Semantic Web. ESWC (Posters) 2008 - [c21]Jan Rauch:
Remarks to Logical Aspects of Measures of Interestingness of Association Rules. ICDM Workshops 2008: 599-608 - [c20]Jan Rauch, Milan Simunek:
LAREDAM - Considerations on System of Local Analytical Reports from Data Mining. ISMIS 2008: 143-149 - [p8]Jan Rauch:
Classes of Association Rules: An Overview. Data Mining: Foundations and Practice 2008: 315-337 - 2007
- [j4]Petr Berka, Jan Rauch, Marie Tomecková:
Lessons Learned from the ECML/PKDD Discovery Challenge on the Atherosclerosis Risk Factors Data. Comput. Informatics 26(3): 329-344 (2007) - [c19]Jan Rauch:
Observational Calculi, Classes of Association Rules and F-property. GrC 2007: 287-293 - [c18]Jan Rauch:
Project SEWEBAR Considerations on Semantic Web and Data Mining. IICAI 2007: 1763-1782 - [c17]Jan Rauch, Milan Simunek:
Semantic Web Presentation of Analytical Reports from Data Mining - Preliminary Considerations. Web Intelligence 2007: 3-7 - 2006
- [c16]Jan Rauch:
Many Sorted Observational Calculi for Multi-Relational Data Mining. ICDM Workshops 2006: 417-422 - [p7]Patrik Eklund, Johan Karlsson, Jan Rauch, Milan Simunek:
On the Logic of Medical Decision Support. Theory and Applications of Relational Structures as Knowledge Instruments 2006: 50-59 - [p6]Jan Rauch:
Definability of Association Rules in Predicate Calculus. Foundations and Novel Approaches in Data Mining 2006: 23-40 - [p5]Jan Rauch, Milan Simunek, Václav Lín:
Mining for Patterns Based on Contingency Tables by KL-Miner - First Experience. Foundations and Novel Approaches in Data Mining 2006: 155-167 - 2005
- [j3]Jan Rauch:
Logic of Association Rules. Appl. Intell. 22(1): 9-28 (2005) - [c15]Vojtech Svátek, Jan Rauch, Martin Ralbovský:
Ontology-Enhanced Association Mining. EWMF/KDO 2005: 163-179 - [c14]Jan Rauch, Milan Simunek:
GUHA method and granular computing. GrC 2005: 630-635 - [p4]Jan Rauch, Milan Simunek:
An Alternative Approach to Mining Association Rules. Foundations of Data Mining and knowledge Discovery 2005: 211-231 - [p3]Petr Strossa, Zdenek Cerný, Jan Rauch:
Reporting Data Mining Results in a Natural Language. Foundations of Data Mining and knowledge Discovery 2005: 347-361 - 2004
- [c13]Vojtech Svátek, Antonín Ríha, Jan Peleska, Jan Rauch:
Analysis of guideline compliance - a data mining approach. CGP 2004: 157-161 - [c12]Tomás Karban, Jan Rauch, Milan Simunek:
SDS-rules and association rules. SAC 2004: 520-524 - [p2]Petr Hájek, Jan Rauch, David Coufal, Thomas Feglar:
The GUHA Method, Data Preprocessing and Mining. Database Support for Data Mining Applications 2004: 135-153 - 2003
- [c11]Petr Strossa, Jan Rauch:
Converting Association Rules into Natural Language - an Attempt. IIS 2003: 383-392 - [p1]Petr Hájek, Martin Holena, Jan Rauch:
The GUHA Method and Foundations of (Relational) Data Mining. Theory and Applications of Relational Structures as Knowledge Instruments 2003: 17-37 - 2002
- [c10]Václav Lín, Jan Rauch, Vojtech Svátek:
Mining and Querying in Association Rule Discovery. KDID 2002: 97-98 - [c9]Václav Lín, Jan Rauch, Vojtech Svátek:
Content-based Retrieval of Analytical Reports. RuleML 2002 - 2001
- [c8]Jan Rauch, Milan Simunek:
Mining for Association Rules by 4ft-Miner. INAP 2001: 285-295 - 2000
- [c7]Jan Rauch, Milan Simunek:
Mining for 4ft Association Rules. Discovery Science 2000: 268-272
1990 – 1999
- 1999
- [c6]Jan Rauch:
Deduction in Logic of Association Rules. ASIAN 1999: 386-387 - [c5]Petr Hájek, Jan Rauch:
Logics and Statistics for Association Rules and Beyond Abstract of Tutorial. PKDD 1999: 586-587 - [e1]Jan M. Zytkow, Jan Rauch:
Principles of Data Mining and Knowledge Discovery, Third European Conference, PKDD '99, Prague, Czech Republic, September 15-18, 1999, Proceedings. Lecture Notes in Computer Science 1704, Springer 1999, ISBN 3-540-66490-4 [contents] - 1998
- [c4]Jan Rauch:
Four-Fold Table Calculi for Discovery Science. Discovery Science 1998: 405-406 - [c3]Jan Rauch:
Classes of Four-Fold Table Quantifiers. PKDD 1998: 203-211 - 1997
- [c2]Jan Rauch:
Logical Calculi for Knowledge Discovery in Databases. PKDD 1997: 47-57 - 1996
- [c1]Jan Rauch:
GUHA as data mining tool. PAKM 1996: 19:1-19:11
1980 – 1989
- 1981
- [j2]Jan Rauch:
Main Problems and Further Possibilities of the Computer Realization of GUHA Procedures. Int. J. Man Mach. Stud. 15(3): 283-287 (1981)
1970 – 1979
- 1975
- [j1]Jan Rauch:
A remark to the GUHA method in the three-valued logic. Kybernetika 11(2): 101-113 (1975)
Coauthor Index
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