Titlebook: Statistical Implicative Analysis; Theory and Applicati Régis Gras,Einoshin Suzuki,Filippo Spagnolo Book 2008 Springer-Verlag Berlin Heidelb

大約冬季

MENT

Pitfalls for Categorizations of Objective Interestingness Measures for Rule Discoverynds of pitfalls: data bias, rule bias, expert bias, and search bias. The main objective of this paper is to issue an alert for the pitfalls which are harmful to one of the most important research topics in data mining. We also list desiderata in categorizing objective interestingness measures.

unstable-angina

處理

Ardent

Didactics of Mathematics and Implicative Statistical Analysis connecting actions, discourses, …, or as a group‘s characteristics. We develop some examples showing how S.I.A. can be used and what special research results it can provide. We insist upon some points that may be interesting methodologically to focus on: asymmetric links, nodes and separate implicative ways.

暴行

The graphic illusion of high school studentsnvolved in this research tend to use the graphical representation of functions. In the context of proportionality, the Statistical Implicative Analysis of students‘ preferences regarding the kind of graphical representation reveals the existence of a graphical illusion shared by high schoolstudents.

后來(lái)

An overview of the Statistical Implicative Analysis (SIA) developmentn of quasi-implications. Originally developed by Gras [11] for applications in the didactics of mathematics, it has considerably evolved and has been applied to a wide range of data, in particular in data mining. This paper is a synthesis which both briefly presents the basic statistical framework o

Vaginismus

outrage

Assessing the interestingness of temporal rules with Sequential Implication Intensityanalysis since the frequent pattern mining algorithms are unsupervised and can produce huge amounts of rules. While association rule interestingness has been widely studied in the literature, there are few measures dedicated to sequential rules. Continuing with our work on the adaptation of implicat

antecedence

Student’s Algebraic Knowledge Modelling: Algebraic Context as Cause of Student’s Actionsronment, which allows students to make freely calculation steps and records all the students‘ actions. One way to build and update the student model is to precisely follow what the student is doing, by means of a detailed representation of cognitive skills. We are interested in persistent and reprod