© 2008

Statistical Implicative Analysis

Theory and Applications

  • Régis Gras
  • Einoshin Suzuki
  • Fabrice Guillet
  • Filippo Spagnolo

Part of the Studies in Computational Intelligence book series (SCI, volume 127)

Table of contents

  1. Front Matter
    Pages I-XXII
  2. Methodology and concepts for SIA

  3. Application to concept learning in education, teaching, and didactics

    1. Marie-Caroline Croset, Jana Trgalova, Jean-François Nicaud Croset
      Pages 75-98
    2. Eduardo Lacasta, Miguel R. Wilhelmi
      Pages 99-117
    3. Catherine-Marie Chiocca, Ingrid Verscheure
      Pages 119-130
    4. Carmen Díaz, Inmaculada de la Fuente, Carmen Batanero
      Pages 163-184
  4. A methodological answer in various application frameworks

    1. Járôme David, Fabrice Guillet, Henri Briand, Régis Gras
      Pages 227-245
    2. Elsa Malisani, Aldo Scimone, Filippo Spagnolo
      Pages 247-276
    3. Dominique Lahanier-Reuter
      Pages 277-298
    4. Stéphane Daviet, Fabrice Guillet, Henri Briand, Serge Baquedano, Vincent Philippé, Régis Gras
      Pages 299-319
    5. Pilar Orús, Pablo Gregori
      Pages 321-345
  5. Extensions to rule interestingness in data mining

About this book


Statistical implicative analysis is a data analysis method created by Régis Gras almost thirty years ago which has a significant impact on a variety of areas ranging from pedagogical and psychological research to data mining. Statistical implicative analysis (SIA) provides a framework for evaluating the strength of implications; such implications are formed through common knowledge acquisition techniques in any learning process, human or artificial. This new concept has developed into a unifying methodology, and has generated a powerful convergence of thought between mathematicians, statisticians, psychologists, specialists in pedagogy and last, but not least, computer scientists specialized in data mining.

This volume collects significant research contributions of several rather distinct disciplines that benefit from SIA. Contributions range from psychological and pedagogical research, bioinformatics, knowledge management, and data mining.


Extension STATISTICA bioinformatics calculus classification clustering data analysis data mining fuzzy knowledge knowledge discovery knowledge management learning modeling ontology

Editors and affiliations

  • Régis Gras
    • 1
  • Einoshin Suzuki
    • 2
  • Fabrice Guillet
    • 3
  • Filippo Spagnolo
    • 4
  1. 1.LINA, FRE 2729 CNRSFrance
  2. 2.Department of InformaticsKyushu UniversityNishiJapan
  3. 3.LINA, FRE 2729 CNRSPolytech'NantesNantesFrance
  4. 4.Dipartimento di MatematicaUnivesritàa di PalermoItaly

Bibliographic information

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