Mining the distribution of features and sorting items by combined attributes are 2 common tasks in exploring and understanding multi-attribute (or multivariate) data. Up to now, few have pointed out the possibility of merging these 2 tasks into a united exploration context and the potential benefits of doing so. In this paper, we present SemanticAxis, a technique that achieves this goal by enabling analysts to build a semantic vector in two-dimensional space interactively. Essentially, the semantic vector is a linear combination of the original attributes. It can be used to represent and explain abstract concepts implied in local (outliers, clusters) or global (general pattern) features of reduced space, as well as serving as a ranking metric for its defined concepts. In order to validate the significance of combining the above 2 tasks in multi-attribute data analysis, we design and implement a visual analysis system, in which several interactive components cooperate with SemanticAxis seamlessly and expand its capacity to handle complex scenarios. We prove the effectiveness of our system and the SemanticAxis technique via 2 practical cases.

Graphic abstract

中文翻译：

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SemanticAxis：通过语义构造和排名分析探索多属性数据

在探索和理解多属性（或多变量）数据中，挖掘特征的分布和按组合属性对项目进行排序是两项常见的任务。到目前为止，很少有人指出将这两个任务合并到一个统一的勘探环境中的可能性以及这样做的潜在好处。在本文中，我们介绍了SemanticAxis，该技术通过使分析人员可以在二维空间中交互构建语义向量来实现此目标。本质上，语义向量是原始属性的线性组合。它可以用来表示和解释减少空间的局部（离群值，聚类）或全局（一般模式）特征中隐含的抽象概念，以及用作其定义概念的排名度量。为了验证在多属性数据分析中结合上述两个任务的重要性，我们设计并实现了一个可视化分析系统，其中多个交互式组件与SemanticAxis无缝协作，并扩展了其处理复杂场景的能力。我们通过2个实际案例证明了我们的系统和SemanticAxis技术的有效性。

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