Visualizing Association Rules Using Linked Matrix,Graph, and Detail Views

• 作者: Orland Hoeber Yoones A. Sekhavat
• 来源期刊: 智能科学国际期刊(英文)
• 学科: 医学
• 关键词: Association Rules Information VISUALIZATION SCALABLE VISUALIZATION Knowledge VISUALIZATION Human Computer Interaction USER EVALUATIONS
• 年，卷（期）: 2013,（1）
• 页码范围: 34-49
• 页数: 16页
• 分类号: R73

摘要

Although association rule mining is an important pattern recognition and data analysis technique, extracting and finding significant rules from a large collection has always been challenging. The ability of information visualization to enable users to gain an understanding of high dimensional and large-scale data can play a major role in the exploration, identification, and interpretation of association rules. In this paper, we propose a method that provides multiple views of the association rules, linked together through a filtering mechanism. A visual inspection of the entire association rule set is enabled within a matrix view. Items of interest can be selected, resulting in their corresponding association rules being shown in a graph view. At any time, individual rules can be selected in either view, resulting in their information being shown in the detail view. The fundamental premise in this work is that by providing such a visual and interactive representation of the association rules, users will be able to find important rules quickly and easily, even as the number of rules that must be inspected becomes large. A user evaluation was conducted which validates this premise.