Mechanizing Hypothesis Formation

The GUHA is a method of mechanizing hypothesis formation. The input of the GUHA procedure consists of analysed data and several parameters defining a large set of relevant patterns. The output is a representation of a set of all relevant patterns satisfying the given true condition.

Auteur

Jan Rauch graduated from the Faculty of Mathematics and Physics of Charles University in Prague. He received his Ph.D. in Mathematical Logic in 1987 from the Institute of Mathematics of the Czechoslovak Academy of Sciences. He is a full professor at the Department of Information and Knowledge Engineering, Prague University of Economics and Business since 2011.

Milan simunek is an associate professor (since 2012) at the Faculty of Informatics and Statistics, Prague University of Economics and Business. His research activities include data mining, databases, virtual reality and software projects development. He is the software project leader of the LISp-Miner system since its launch in 1996 and author of its core-modules implementation.

David Chudán is an assistant professor of Applied Informatics at the Faculty of Informatics and Statistics, Prague University of Economics and Business. He received his Ph.D. in 2015 in the field of Applied informatics. His research interests include data mining and machine learning on different tools and platforms. Another research area is GUHA association rules and their complementary usage with business intelligence.

Petr MáSa graduated from the Prague University of Economics and Business and the Faculty of Mathematics and Physics of Charles University in Prague. He received his Ph.D. in 2006. He also works on business projects where he uses data mining, data science, data analytics and he is also business responsible.