A new metaphor for projection-based visual analysis and data exploration
A new metaphor for projection-based visual analysis and data exploration
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DOI：
10.1117/12.697879
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发表时间：
2007-01
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影响因子：
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通讯作者：
Tobias Schreck;Christian Panse
中科院分区：
文献类型：
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作者：
Tobias Schreck;Christian Panse
在许多重要的应用领域，如商业和金融、流程监控和安全，收集到了海量且快速增长的复杂数据。目前正在大力开发自动化和交互式分析工具，以便从这些数据储存库中挖掘有用的信息。许多数据分析算法需要适当定义数据实例之间的相似性(或距离)，以便在其他分析任务中实现有意义的聚类、分类和检索。基于投影的数据可视化对于(A)对于给定相似性定义内的数据集的视觉辨别分析，以及(B)对于由不同相似性定义表示的给定数据集的相似性特征的比较分析是非常有趣的。我们介绍了一种直观而有效的基于投影的相似性可视化方法，用于交互判别分析、数据探索和度量空间有效性的可视化评估。该方法基于凸壳隐喻，用于可视化地聚合投影空间中的点集，并且它可以与各种不同的投影技术一起使用。在两个已知数据集上的应用表明了该方法的有效性。给出了支持船体隐喻有效性的统计证据。我们提倡基于外壳的方法而不是标准的基于符号的方法来进行投影可视化，因为它允许更有效地感知相似关系和类分布特征。
In many important application domains such as Business and Finance, Process Monitoring, and Security, huge and quickly increasing volumes of complex data are collected. Strong efforts are underway developing automatic and interactive analysis tools for mining useful information from these data repositories. Many data analysis algorithms require an appropriate definition of similarity (or distance) between data instances to allow meaningful clustering, classification, and retrieval, among other analysis tasks. Projection-based data visualization is highly interesting (a) for visual discrimination analysis of a data set within a given similarity definition, and (b) for comparative analysis of similarity characteristics of a given data set represented by different similarity definitions. We introduce an intuitive and effective novel approach for projection-based similarity visualization for interactive discrimination analysis, data exploration, and visual evaluation of metric space effectiveness. The approach is based on the convex hull metaphor for visually aggregating sets of points in projected space, and it can be used with a variety of different projection techniques. The effectiveness of the approach is demonstrated by application on two well-known data sets. Statistical evidence supporting the validity of the hull metaphor is presented. We advocate the hull-based approach over the standard symbol-based approach to projection visualization, as it allows a more effective perception of similarity relationships and class distribution characteristics.