Statistical Implicative Analysis : Theory and Applications

Statistical Implicative Analysis Theory and Applications

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 evaluatin...

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Detalles Bibliográficos
Autor Corporativo: SpringerLink (-)
Otros Autores: Gras, Régis (-), Suzuki, Einoshin, Guillet, Fabrice, Spagnolo, Filippo
Formato: Libro electrónico
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg 2008.
Colección:Studies in Computational Intelligence ; 127.
Springer eBooks.
Acceso en línea:Conectar con la versión electrónica
Ver en Universidad de Navarra:https://innopac.unav.es/record=b33036287*spi
Tabla de Contenidos:
  • Methodology and concepts for SIA
  • An overview of the Statistical Implicative Analysis (SIA) development
  • CHIC: Cohesive Hierarchical Implicative Classification
  • Assessing the interestingness of temporal rules with Sequential Implication Intensity
  • Application to concept learning in education, teaching, and didactics
  • Student's Algebraic Knowledge Modelling: Algebraic Context as Cause of Student's Actions
  • The graphic illusion of high school students
  • Implicative networks of student's representations of Physical Activities
  • A comparison between the hierarchical clustering of variables, implicative statistical analysis and confirmatory factor analysis
  • Implications between learning outcomes in elementary bayesian inference
  • Personal Geometrical Working Space: a Didactic and Statistical Approach
  • A methodological answer in various application frameworks
  • Statistical Implicative Analysis of DNA microarrays
  • On the use of Implication Intensity for matching ontologies and textual taxonomies
  • Modelling by Statistic in Research of Mathematics Education
  • Didactics of Mathematics and Implicative Statistical Analysis
  • Using the Statistical Implicative Analysis for Elaborating Behavioral Referentials
  • Fictitious Pupils and Implicative Analysis: a Case Study
  • Identifying didactic and sociocultural obstacles to conceptualization through Statistical Implicative Analysis
  • Extensions to rule interestingness in data mining
  • Pitfalls for Categorizations of Objective Interestingness Measures for Rule Discovery
  • Inducing and Evaluating Classification Trees with Statistical Implicative Criteria
  • On the behavior of the generalizations of the intensity of implication: A data-driven comparative study
  • The TVpercent principle for the counterexamples statistic
  • User-System Interaction for Redundancy-Free Knowledge Discovery in Data
  • Fuzzy Knowledge Discovery Based on Statistical Implication Indexes.

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