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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Statistical Implicative Analysis: Theory and Applications

Statistical Implicative Analysis: Theory and Applications

Gras, Régis
Guillet, Fabrice
Suzuki, Einoshin

Hardcover

Series: Studies in Computational Intelligence, Book 127

Technology & EngineeringGeneral ComputersGeneral Mathematics

ISBN10: 3540789820
ISBN13: 9783540789826
Publisher: Springer
Published: Apr 29 2008
Pages: 513
Weight: 2.02
Height: 1.13 Width: 6.14 Depth: 9.21
Language: English
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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