Mechanizing Hypothesis Formation (ePub)
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.
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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.
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.
- Autoren: Jan Rauch , Milan Simunek , David Chudán , Petr Mása
- 2022, 362 Seiten, Englisch
- Verlag: Taylor & Francis
- ISBN-10: 1000778029
- ISBN-13: 9781000778021
- Erscheinungsdatum: 20.10.2022
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: ePub
- Größe: 19 MB
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