Mechanizing Hypothesis Formation: Principles and Case Studies

Mechanizing hypothesis formation is an approach to exploratory data analysis. Its development started in the 1960s inspired by the question ＂can computers formulate and verify scientific hypotheses?＂. The development resulted in a general theory of logic of discovery. It comprises theoretical calculi dealing with theoretical statements as well as observational calculi dealing with observational statements concerning finite results of observation. Both calculi are related through statistical hypotheses tests. A GUHA method is a tool of the logic of discovery. It uses a one-to-one relation between theoretical and observational statements to get all interesting theoretical statements. A GUHA procedure generates all interesting observational statements and verifies them in a given observational data. Output of the procedure consists of all observational statements true in the given data. Several GUHA procedures dealing with association rules, couples of association rules, action rules, histograms, couples of histograms, and patterns based on general contingency tables are involved in the LISp-Miner system developed at the Prague University of Economics and Business. Various results about observational calculi were achieved and applied together with the LISp-Miner system.

The book covers a brief overview of logic of discovery. Many examples of applications of the GUHA procedures to solve real problems relevant to data mining and business intelligence are presented. An overview of recent research results relevant to dealing with domain knowledge in data mining and its automation is provided. Firsthand experiences with implementation of the GUHA method in the Python language are presented.

Jan Rauch graduated from the Faculty of Mathematics and Physics of Charles University in 1972, majoring in numerical mathematics. He received his doctorate in mathematical logic in 1987 at the Institute of Mathematics of the Czechoslovak Academy of Sciences in Prague. Since 1993, he is a member of the Department of Information and Knowledge Engineering, Faculty of Informatics and Statistics, Prague University of Economics and Business. He was appointed as professor of informatics in 2011. His research interests include data mining, GUHA method, observational calculi, and their applications.

Milan Simůnek is an associate professor (since 2012) at the Faculty Informatics and Statistics, Prague University of Economics and Business. He graduated in 2001 and obtained a Ph.D. in Applied Informatics in 2004. His research and educational activities concern 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 software implementation.

David Chudán graduated from the Faculty of Informatics and Statistics of the Prague University of Economics and Business in 2008. He received his doctorate in applied informatics in 2015 at the same university. Since 2015, he has been a member of the Department of Information and Knowledge Engineering, Faculty of Informatics and Statistics of the Prague University of Economics and Business. His research interest inlude data mining and machine learning, the relationship between data mining and business intelligence and cloud machine learning platforms.

Petr Mása graduated from the Faculty of Informatics and Statistics at Prague University of Economics and Business in 2001 and Faculty of Mathematics and Physics at Charles University in 2003. He received his doctorate in informatics in 2006 at the Prague University of Economics and Business. Since 2001 he is working for commercial companies where he applies data mining, he was both IT and business responsible, he has experience from startups and corporates as well as consulting companies, including Big 4 companies. Since 2006 he is teaching at Prague University of Economics and Business.