Visual clustering in parallel coordinates and graphs
Visual clustering in parallel coordinates and graphs
复制标题
平行坐标和图形中的视觉聚类
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Hong Zhou
中科院分区：
文献类型：
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作者：
Huamin Qu;Hong Zhou
近年来，信息可视化已经成为多元和关系数据分析的一个非常活跃的研究领域。它将复杂和抽象的数据，如人口统计数据，财务数据，社交网络和论文引用转化为可视化表示，然后用户可以利用交互式计算机图形技术和人类视觉能力来深入了解数据。平行坐标和图形是信息可视化中两种成熟的方法。然而，当数据变得非常大时，这两种方法的有效性都会大大降低，因为成千上万的线条很容易淹没显示器，并且由此产生的视觉混乱会掩盖任何潜在的模式。因此，平行坐标和图形的杂波抑制是信息可视化中一个非常重要的研究问题。
在这篇论文中，我们介绍了视觉聚类作为一种新的方法来减少杂波和模式检测。与传统的杂波抑制方法（如过滤和刷）相比，视觉聚类可以增强和揭示数据中的有趣模式，同时保留上下文。对于平行坐标，我们提出了一种基于力的优化方法，通过调整它们的形状来捆绑折线，以及一个飞溅框架来显示动画功能。对于图，提出了一种基于几何的边分组方法和一种基于能量的层次视觉聚类方案。这些方法的有效性已被证明通过广泛的实验，使用合成数据和数据集从真实的应用。
Information visualization has emerged as a very active research field for multivariate and relational data analysis in recent years. It turns complex and abstract data such as demographic data, financial data, social networks, and paper citations into visual representations, and then users can exploit interactive computer graphics techniques and human visual capabilities to gain insight into the data. Parallel coordinates and graphs are two well-established methods in information visualization. However, when data become very large, the effectiveness of both methods is dramatically reduced as tens of thousands of lines can easily overwhelm the display and the resulting visual clutter will obscure any underlying patterns. Thus, clutter reduction for parallel coordinates and graphs is a very important research problem in information visualization.
In this thesis, we introduce visual clustering as a new approach for clutter reduction and pattern detection. Compared with traditional clutter reduction methods such as filtering and brushing, visual clustering can enhance and reveal interesting patterns in the data while preserving the context. For parallel coordinates, we present a force-based optimization method to bundle polylines by adjusting their shapes, and a splatting framework to reveal features with animations. For graphs, a geometry-based edge grouping approach and an energy-based hierarchical visual clustering scheme are proposed. The effectiveness of these methods has been demonstrated through extensive experiments using both synthetic data and datasets from real applications.