Statistical Implicative Analysis: Theory and Applications / Edition 1 available in Hardcover
- Pub. Date:
- Springer Berlin Heidelberg
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.
Table of Contents
Part I Methodology and concepts for SIA.- Part II Application to concept learning in education, teaching, and Didactics.- Part III A methodological answer in various application Frameworks.- Part IV Extensions to rule interestingness in data mining.