Mining itemsets for association rule generation is a fundamental data mining task originally stemming from the traditional market basket analysis problem. However, enumerating all frequent itemsets, especially in a dense dataset, or with low support thresholds, remains costly. In this paper, a novel theorem builds the relationship between frequent closed itemsets (FCIs) and frequent generator itemsets (FGIs) and proves that the process of mining FCIs is equivalent to mining FGIs, unified with their full-support and extension items. On the basis of this theorem, a generator-based algorithm for mining FCIs, called GrAFCI⁺, is proposed and explained in details including its correctness. The comparative effectiveness of the algorithm in terms of scalability is first investigated, along with the compression rate—a measure of the interestingness of a given FIs representation. Extensive experiments are further undertaken on eight datasets and four state-of-the-art algorithms, namely DCI_CLOSED*, DCI_PLUS, FPClose, and NAFCP. The results show that the proposed algorithm is more efficient regarding the execution time in most cases as compared to these algorithms. Because GrAFCI⁺ main goal is to address the runtime issue, it paid a memory cost, especially when the support is too small. However, this cost is not high since GrAFCI⁺ is seconded by only one competitor out of four in memory utilization and for large support values. As an overall assessment, GrAFCI⁺ gives better results than most of its competitors.

The purpose of the present research is trying to determine if the reading comprehension instruction can be enhanced by an online reading environment to compensate for the lack of time devoted to the English course at Algeria, Batna Computer Science Department. Our main research question is trying to provide us with an answer to the following question whether an online learning of English as a Foreign Language university students’ English reading comprehension should be embraced or avoided. The findings of the experiment revealed by the ANOVA’s (ANalyse of VAriance) method that from practical side, a true integration of the Web in English language pedagogy can help poor students better their English and become somehow independent and confident readers and incite the teacher to be active in the teaching learning process. The paper concludes with some suggestions and recommendations we think, they are necessary for the implementation of an online reading comprehension environment in Algerian universities.

Cet article traite de la problématique du soutien à apporter à l’apprenant en situation de formation à distance asynchrone sur le web. Nous présentons à cet effet un système tuteur intelligent (STI) basé sur la technologie hypermédia, construit dans le cadre de la plate-forme d’enseignement à distance IBN SINA. Ce STI construit selon une architecture client-serveur, consiste en un canevas générique que les auteurs instancient à travers un mode auteur coopératif. Construit autour des technologies XML et Systèmes Experts, ce système permet une adaptation dynamique de l’activité d’apprentissage, et ce, en se basant sur trois modèles : un modèle pédagogique (la pédagogie par objectif), un modèle de métadonnées (le standard LOM) et un modèle de l’apprenant (de type overlay). Après avoir présenté la problématique de soutien à l’apprenant à distance, nous décrivons brièvement la modélisation de notre système ainsi que son architecture logicielle.