A scalable association rule visualization towards displaying large amounts of knowledge

Olivier Couturier, T. Hamrouni, S. Yahia, E. Nguifo
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引用次数: 33

Abstract

Providing efficient and easy-to-use graphical tools to users is a promising challenge of data mining (DM). These tools must be able to generate explicit knowledge and to restitute it. Visualization techniques have shown to be an efficient solution to achieve such goal. Even though considered as a key step in the mining process, the visualization step of association rules received much less attention than that paid to the extraction one. Nevertheless, some graphical tools have been developed to extract and visualize association rules. In those tools, various approaches are proposed to filter the huge number of association rules before the visualization step. However both DM steps (association rule extraction and visualization) are treated separately in a one way process. Our approach differs, and uses meta-knowledge to guide the user during the mining process. Standing at the crossroads of DM and Human-Computer Interaction (HCI), we present an integrated framework covering both steps of the DM process. Furthermore, our approach can easily integrate previous techniques of association rule visualization.
用于显示大量知识的可伸缩关联规则可视化
向用户提供高效且易于使用的图形工具是数据挖掘(DM)的一个有前途的挑战。这些工具必须能够产生明确的知识并恢复它。可视化技术已被证明是实现这一目标的有效解决方案。尽管关联规则的可视化被认为是挖掘过程中的关键步骤,但其受到的关注却远远少于关联规则的提取。然而,已经开发了一些图形工具来提取和可视化关联规则。在这些工具中,提出了各种方法来在可视化步骤之前过滤大量的关联规则。然而,DM的两个步骤(关联规则提取和可视化)在单向过程中被单独处理。我们的方法不同,在挖掘过程中使用元知识来指导用户。站在DM和人机交互(HCI)的十字路口,我们提出了一个涵盖DM过程两个步骤的集成框架。此外,我们的方法可以很容易地集成以前的关联规则可视化技术。
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