二分数据集的BiFold可视化
BiFold visualization of bipartite datasets
摘要：
新兴的数据科学领域需要开发用于知识发现的算法和工具。人类通过构造良好的图形表示与数据进行交互，可以利用我们识别模式的视觉能力。我们开发了一个数据可视化框架，称为BiFold，用于描述对象组之间二元关系的二分数据集的探索性分析。典型的数据示例包括投票记录、组织成员资格、成对关联或其他二进制数据集。BiFold提供了一种低维的数据嵌入，通过视觉接近度表示相似性，类似于多维缩放（MDS）。BiFold的独特和新功能是它能够同时捕获对象之间的组内和组间关系，增强知识发现。我们使用{ \it Southern Women Dataset}对BiFold进行基准测试，其中社会群体现在在视觉上很明显。我们为两个美国投票数据集构建了BiFold图：对于1976年以来的总统选举结果，BiFold说明了这些选举结果背后不断演变的地缘政治结构。对于参议院国会投票，BiFold确定了一个党派坐标，将参议员分为两党，同时可视化两党联盟坐标，该坐标捕捉了法案的最终命运（通过/失败）。最后，我们考虑了一个全球烹饪数据集的食谱和食品成分之间的关联。BiFold使我们能够在视觉上比较和对比美食，同时还可以识别个别美食的标志性成分。
英文摘要：
The emerging domain of data-enabled science necessitates development of algorithms and tools for knowledge discovery. Human interaction with data through well-constructed graphical representation can take special advantage of our visual ability to identify patterns. We develop a data visualization framework, called BiFold, for exploratory analysis of bipartite datasets that describe binary relationships between groups of objects. Typical data examples would include voting records, organizational memberships, and pairwise associations, or other binary datasets. BiFold provides a low dimensional embedding of data that represents similarity by visual nearness, analogous to Multidimensional Scaling (MDS). The unique and new feature of BiFold is its ability to simultaneously capture both within-group and between-group relationships among objects, enhancing knowledge discovery. We benchmark BiFold using the {\it Southern Women Dataset}, where social groups are now visually evident. We construct BiFold plots for two US voting datasets: For the presidential election outcomes since 1976, BiFold illustrates the evolving geopolitical structures that underlie these election results. For Senate congressional voting, BiFold identifies a partisan coordinate, separating senators into two parties while simultaneously visualizing a bipartisan-coalition coordinate which captures the ultimate fate of the bills (pass/fail). Finally, we consider a global cuisine dataset of the association between recipes and food ingredients. BiFold allows us to visually compare and contrast cuisines while also allowing identification of signature ingredients of individual cuisines.
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