Optimizing occlusion appearances in 3D association rules visualization

Olivier Couturier, Vincent Dubois, Tien-Ning Hsu, E. Nguifo
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引用次数: 7

Abstract

Providing efficient and easy-to-use graphical tools to users is a promising challenge for data mining (DM). Visual data mining (VDM) is a new and active research area which goal is to provide powerful and suitable tools for data miners. Some graphical tools have been developed to extract and visualize association rules (AR), among which a three dimension representation where the x-axis is the AR premise, the y-axis is the AR conclusion and the z-axis is a metric value of AR. The 3D approach is one standard representation that is often implemented in many DM tools. However this approach suffers from an overlapping between several objects in the 3D space making some objects unseen or partially truncated. This problem is known as the occlusion problem. In this paper, we propose to formalize it as an optimisation problem of occlusions. Then we define conditions to limit occlusions and finally we propose different heuristics based on ordering of axis-elements, to considerably reduce the number of generated occlusions.
三维关联规则可视化中遮挡现象的优化
为用户提供高效且易于使用的图形工具是数据挖掘(DM)面临的一个很有前途的挑战。可视化数据挖掘(Visual data mining, VDM)是一个新兴的、活跃的研究领域,其目标是为数据挖掘者提供强大的、合适的工具。已经开发了一些图形工具来提取和可视化关联规则(AR),其中x轴是AR前提,y轴是AR结论,z轴是AR度量值的三维表示。3D方法是许多DM工具中经常实现的一种标准表示。然而,这种方法受到3D空间中多个对象之间重叠的影响,使得一些对象看不见或部分截断。这个问题被称为遮挡问题。在本文中,我们建议将其形式化为遮挡的优化问题。然后我们定义了限制遮挡的条件,最后我们提出了基于轴元素排序的不同启发式方法,以大大减少生成的遮挡数量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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