Interactive visual exploration of association rules with rule-focusing methodology

作     者：Blanchard, Julien Guillet, Fabrice Briand, Henri 

作者机构：Ecole Polytech Univ Nantes Dept Informat F-44306 Nantes France 

出 版 物：《KNOWLEDGE AND INFORMATION SYSTEMS》 (知识和信息系统季刊)

年 卷 期：2007年第13卷第1期

页      面：43-75页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 0701[理学-数学] 071101[理学-系统理论] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：knowledge discovery in databases association rules post-processing interactive visualization rule focusing constraint-based mining interestingness measures neighborhood of rules 

摘      要：On account of the enormous amounts of rules that can be produced by data mining algorithms, knowledge post-processing is a difficult stage in an association rule discovery process. In order to find relevant knowledge for decision making, the user (a decision maker specialized in the data studied) needs to rummage through the rules. To assist him/her in this task, we here propose the rule-focusing methodology, an interactive methodology for the visual post-processing of association rules. It allows the user to explore large sets of rules freely by focusing his/her attention on limited subsets. This new approach relies on rule interestingness measures, on a visual representation, and on interactive navigation among the rules. We have implemented the rule-focusing methodology in a prototype system called ARVis. It exploits the user s focus to guide the generation of the rules by means of a specific constraint-based rule-mining algorithm.