Cube Based Summaries of Large Association Rule Sets

摘要：

A major problem when dealing with association rules postprocessing is the huge amount of extracted rules. Several approaches have been implemented to summarize them. However, the obtained summaries are generally difficult to analyse because they suffer from the lack of navigational tools. In this paper, we propose a novel method for summarizing large sets of association rules. Our approach enables to obtain from a rule set, several summaries called Cube Based Summaries (CBSs). We show that the CBSs can be represented as cubes and we give an overview of OLAP navigational operations that can be used to explore them. Moreover, we define a new quality measure called homogeneity, to evaluate the interestingness of CBSs. Finally, we propose an algorithm that generates a relevant CBS w.r.t. a quality measure, to initialize the exploration. The evaluation of our algorithm on benchmarks proves the effectiveness of our approach.

关键词： Association rules summary cubes

作者: Marie Ndiaye Cheikh T. Diop Arnaud Giacometti Patrick Marcel Arnaud Soulet

作者单位: Laboratoire dInformatique Universite Francois Rabelais Tours,Antenne Universitaire de Blois 3 place Laboratoire dAnalyse Numérique et dInformatique,Université Gaston Berger de Saint-Louis BP 234 Sai Laboratoire dInformatique Universite Francois Rabelais Tours,Antenne Universitaire de Blois 3 place

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 73-85

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）