大型树状图与原型的互动探索

Andee Kaplan, J. Bien
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摘要

层次聚类是用于识别和探索数据集中可能存在的底层结构的标准方法之一。向学生们展示了一些例子,其中树形图是分层聚类的可视化表示,揭示了一个清晰的聚类结构。然而,在实践中,今天的数据分析师经常遇到数据集的大规模破坏了树形图作为可视化工具的有用性。密集的分支模糊了结构,重叠的标签无法阅读。在本文中,我们提出了一个新的工作流程,通过R包protoshiny来执行分层聚类,旨在将分层聚类恢复到以前作为有效和通用可视化工具的角色。我们的建议利用了交互性,并结合了用代表性数据点(称为原型)标记树形图中的内部节点的能力。在介绍了工作流之后,我们将提供三个案例研究来演示它的实用性。
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Interactive Exploration of Large Dendrograms with Prototypes
ABSTRACT Hierarchical clustering is one of the standard methods taught for identifying and exploring the underlying structures that may be present within a dataset. Students are shown examples in which the dendrogram, a visual representation of the hierarchical clustering, reveals a clear clustering structure. However, in practice, data analysts today frequently encounter datasets whose large scale undermines the usefulness of the dendrogram as a visualization tool. Densely packed branches obscure structure, and overlapping labels are impossible to read. In this article we present a new workflow for performing hierarchical clustering via the R package called protoshiny that aims to restore hierarchical clustering to its former role of being an effective and versatile visualization tool. Our proposal leverages interactivity combined with the ability to label internal nodes in a dendrogram with a representative data point (called a prototype). After presenting the workflow, we provide three case studies to demonstrate its utility.
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