Working with patterns in large multivariate datasets - Karnaugh-Veitch-Maps revisited

T. May
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引用次数: 5

Abstract

We present an interactive visualization method for the multivariate analysis of large and complex datasets, based on the layout of Karnaugh-Veitch-diagrams. Working on data categories, we additionally provide an interactive partitioning of value ranges of ordinal types. Multivariate dependencies manifest on the map in characteristic color patterns. These patterns are representations of subsets of the data or attributes which embody significant information. While the task of the user is to identify visual patterns of particular interest to him by clicking on the map, the software identifies a minimal representation of the corresponding subset and gives a visual feedback. Hence, user and machine cooperate on the basis of a strong visual coupling in short iterative cycles. During this process the trade-off between the accuracy and simplicity of a representation, which is crucial for any type of the building of analytical models, can be found in an effective way.
处理大型多元数据集中的模式——重新审视Karnaugh-Veitch-Maps
我们提出了一种基于karnov - veitch图布局的交互式可视化方法,用于大型复杂数据集的多变量分析。在处理数据类别时,我们还提供了序数类型值范围的交互式分区。多变量依赖关系在地图上以特征颜色模式显示。这些模式是包含重要信息的数据子集或属性的表示。当用户的任务是通过点击地图来识别他特别感兴趣的视觉模式时,软件识别相应子集的最小表示并给出视觉反馈。因此,用户和机器在短迭代周期内以强视觉耦合为基础进行合作。在此过程中,可以以一种有效的方式找到表示的准确性和简单性之间的权衡,这对于任何类型的分析模型的构建都是至关重要的。
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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