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portada Mechanizing Hypothesis Formation: Principles and Case Studies
Type
Physical Book
Publisher
Author
Language
English
Pages
346
Format
Paperback
ISBN13
9780367549824
Edition No.
1

Mechanizing Hypothesis Formation: Principles and Case Studies

Jan Rauch; Milan Šimůnek; David Chudán; Petr Máša (Author) · Crc Press · Paperback

Mechanizing Hypothesis Formation: Principles and Case Studies - Jan Rauch; Milan Šimůnek; David Chudán; Petr Máša

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Synopsis "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.

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