Observational Calculi and Association Rules

  • Jan Rauch

Part of the Studies in Computational Intelligence book series (SCI, volume 469)

Table of contents

  1. Front Matter
    Pages 1-20
  2. Jan Rauch
    Pages 1-13
  3. Logical Calculi of Association Rules

    1. Front Matter
      Pages 15-16
    2. Jan Rauch
      Pages 17-20
    3. Jan Rauch
      Pages 27-38
    4. Jan Rauch
      Pages 39-61
  4. Classes of Association Rules

    1. Front Matter
      Pages 63-64
    2. Jan Rauch
      Pages 65-79
    3. Jan Rauch
      Pages 81-97
    4. Jan Rauch
      Pages 99-126
    5. Jan Rauch
      Pages 127-147
    6. Jan Rauch
      Pages 149-154
  5. Results on Classes of Association Rules

    1. Front Matter
      Pages 155-156
    2. Jan Rauch
      Pages 157-180
    3. Jan Rauch
      Pages 181-200
  6. Applications and Research Challenges

    1. Front Matter
      Pages 231-232
    2. Jan Rauch
      Pages 233-260
    3. Jan Rauch
      Pages 261-271
    4. Jan Rauch
      Pages 273-282
  7. Back Matter
    Pages 0--1

About this book


Observational calculi were introduced in the 1960’s as a tool of logic of discovery. Formulas of observational calculi correspond to assertions on analysed data. Truthfulness of suitable assertions can lead to acceptance of new scientific hypotheses. The general goal was to automate the process of discovery of scientific knowledge using mathematical logic and statistics. The GUHA method for producing true formulas of observational calculi relevant to the given problem of scientific discovery was developed. Theoretically interesting and practically important results on observational calculi were achieved. Special attention was paid to formulas - couples of Boolean attributes derived from columns of the analysed data matrix. Association rules introduced in the 1990’s can be seen as a special case of such formulas. New results on logical calculi and association rules were achieved. They can be seen as a logic of association rules. This can contribute to solving contemporary challenging problems of data mining research and practice. The book covers thoroughly the logic of association rules and puts it into the context of current research in data mining. Examples of applications of theoretical results to real problems are presented. New open problems and challenges are listed. Overall, the book is a valuable source of information for researchers as well as for teachers and students interested in data mining.


Association Rules Computational Intelligence Data Mining Observational calculi

Authors and affiliations

  • Jan Rauch
    • 1
  1. 1.University of Economics, PraguePragueCzech Republic

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-642-11736-7
  • Online ISBN 978-3-642-11737-4
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
Industry Sectors
Materials & Steel
Chemical Manufacturing
Finance, Business & Banking
IT & Software
Consumer Packaged Goods
Energy, Utilities & Environment
Oil, Gas & Geosciences