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.
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.
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 im¿nek 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 Máa 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.
Inhaltsangabe
1. Introduction 2. Data Sets SECTION I: THE GUHA PROCEDURES 3. Principle and Simple Examples 4. Common Features 5. LISp Miner System SECTION II: APPLYING THE GUHA PROCEDURES 6. Examples Overview 7. 4ft Miner GUHA Association Rules 8. CF Miner Histograms 9. KL Miner Pairs of Categorical Attributes 10. SD 4ft Miner Couples of GUHA Association Rules 11. SDCF Miner Couples of Histograms 12. SDKL Miner Couples of Pairs of Categorical Attributes 13. Ac4ft Miner Action Rules 14. GUHA Procedures and Business Intelligence 15. CleverMiner GUHA and Python SECTION III: RELATED RESEARCH AND THEORY 16. Artificial Data Generation and LM ReverseMiner Module 17. Applying Domain Knowledge 18. Observational Calculi
1. Introduction 2. Data Sets SECTION I: THE GUHA PROCEDURES 3. Principle and Simple Examples 4. Common Features 5. LISp Miner System SECTION II: APPLYING THE GUHA PROCEDURES 6. Examples Overview 7. 4ft Miner GUHA Association Rules 8. CF Miner Histograms 9. KL Miner Pairs of Categorical Attributes 10. SD 4ft Miner Couples of GUHA Association Rules 11. SDCF Miner Couples of Histograms 12. SDKL Miner Couples of Pairs of Categorical Attributes 13. Ac4ft Miner Action Rules 14. GUHA Procedures and Business Intelligence 15. CleverMiner GUHA and Python SECTION III: RELATED RESEARCH AND THEORY 16. Artificial Data Generation and LM ReverseMiner Module 17. Applying Domain Knowledge 18. Observational Calculi
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