Earticle

초록

영어

Mining association rules is a task of data mining, which extracts knowledge in the form of significant implication relation of useful items (objects) from a database. Mining multilevel association rules uses concept hierarchies, also called taxonomies and defined as relations of type 'is-a' between objects, to extract rules that items belong to different levels of abstraction. These rules are more useful, more refined and more interpretable by the user. Several algorithms have been proposed in the literature to discover the multilevel association rules. In this article, we are interested in the problem of discovering multi-level frequent itemsets under constraints, involving the user in the research process. We proposed a technique for modeling and interpretation of constraints in a context of use of concept hierarchies. Three approaches for discovering multi-level frequent itemsets under constraints were proposed and discussed: Basic approach, “Test and Generate” approach and Pruning based Approach.

목차

ABSTRACT
 1. Introduction
 2. Mining multi-level association rules
  2.1. Problem specification
  2.2. Algorithms for mining multi-level association rules: An Overview
 3. Algorithms for Mining Frequent Multi-level Itemsets underConstraints
  3.1. Modeling the constraints of existence on association rules
  3.2. Modeling the constraints of existence in a context of use of concept hierarchies
  3.3. Algorithms for mining frequent multi-level itemsets under constraints
 4. Conclusion
 References

키워드

저자

• Mohamed Salah GOUIDER [ BESTMOD Laboratory Institut Supérieur de Gestion ]
• Amine FARHAT [ Institut Supérieur de Gestion ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605