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.
Author: Régis Gras
Publisher: Springer Science & Business Media
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.
Note Interne 47, Centre d'Analyse Documentaire pour l'Archéologie, C.N.R.S., Marseille (1971) 5. ... Gras, R., Kuntz, P.: An overview of the statistical implicative analysis development. In: Guillet, F., Gras, R., Suzuki, E., Spagnolo, ...
Author: Israël César Lerman
This book offers an original and broad exploration of the fundamental methods in Clustering and Combinatorial Data Analysis, presenting new formulations and ideas within this very active field. With extensive introductions, formal and mathematical developments and real case studies, this book provides readers with a deeper understanding of the mutual relationships between these methods, which are clearly expressed with respect to three facets: logical, combinatorial and statistical. Using relational mathematical representation, all types of data structures can be handled in precise and unified ways which the author highlights in three stages: Clustering a set of descriptive attributes Clustering a set of objects or a set of object categories Establishing correspondence between these two dual clusterings Tools for interpreting the reasons of a given cluster or clustering are also included. Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering will be a valuable resource for students and researchers who are interested in the areas of Data Analysis, Clustering, Data Mining and Knowledge Discovery.
Cépaduès (2004) Lerman, I.C.: Sur l'analyse des données préalable à une classification automatique; proposition d'une nouvelle mesure de similarité. Mathématiques et Sciences Humaines 8, ... Statistical Implicative Analysis, pp.449–462.
Author: Fabrice Guillet
Category: Technology & Engineering
The recent and novel research contributions collected in this book are extended and reworked versions of a selection of the best papers that were originally presented in French at the EGC’2011 Conference held in Brest, France, on January 2011. EGC stands for "Extraction et Gestion des connaissances" in French, and means "Knowledge Discovery and Management" or KDM. KDM is concerned with the works in computer science at the interface between data and knowledge; such as Data Mining, Knowledge Discovery, Business Intelligence, Knowledge Engineering and Semantic Web. This book is intended to be read by all researchers interested in these fields, including PhD or MSc students, and researchers from public or private laboratories. It concerns both theoretical and practical aspects of KDM. This book has been structured in two parts. The first part, entitled “Data Mining, classification and queries”, deals with rule and pattern mining, with topological approaches and with OLAP. The second part of the book, entitled “Ontology and Semantic”, is related to knowledge-based and user-centered approaches in KDM.
Our re-analysis of the Weiss sample data produced some interesting results but no dramatic turnarounds of substantive conclusions. These are reported in Table 13.2. James-Stein estimates are calculated for equations (13.2.1), ...
Meta-analysis employs statistical techniques for aggregating data and for determining relationships between causal variables and outcomes. Usually, the first step in a meta-analysis is the precise description of a population of studies ...
Leverage R as a powerful statistical tool Test your hypotheses and draw conclusions Use R to give meaning to your data The easy, practical guide to R R is powerful, free software for statistical analysis full of many tools and functions.
Author: Joseph Schmuller
Publisher: John Wiley & Sons
Understanding the world of R programming and analysis has never been easier Most guides to R, whether books or online, focus on R functions and procedures. But now, thanks to Statistical Analysis with R For Dummies, you have access to a trusted, easy-to-follow guide that focuses on the foundational statistical concepts that R addresses—as well as step-by-step guidance that shows you exactly how to implement them using R programming. People are becoming more aware of R every day as major institutions are adopting it as a standard. Part of its appeal is that it's a free tool that's taking the place of costly statistical software packages that sometimes take an inordinate amount of time to learn. Plus, R enables a user to carry out complex statistical analyses by simply entering a few commands, making sophisticated analyses available and understandable to a wide audience. Statistical Analysis with R For Dummies enables you to perform these analyses and to fully understand their implications and results. Gets you up to speed on the #1 analytics/data science software tool Demonstrates how to easily find, download, and use cutting-edge community-reviewed methods in statistics and predictive modeling Shows you how R offers intel from leading researchers in data science, free of charge Provides information on using R Studio to work with R Get ready to use R to crunch and analyze your data—the fast and easy way!
Statistical Implicative Analysis : Theory and Appli- R. Gras cations , Studies in Computational Intelligence , 127 F. Guillet E. Suzuki F. S. ( eds . ) Advances in Knowledge Discovery and Data Mining , LNAI 5012 , ( Springer - Verlag ) ...
aspects of statistics covering data analysis in particular . The author of the present paper pointed out some features of the relativistic logic of mutual specification in his paper [ 12 ] , and it seem to us to be even more important ...