论文标题
语义：通过语义构建和排名分析探索多属性数据
SemanticAxis: Exploring Multi-attribute Data by Semantics Construction and Ranking Analysis
论文作者
论文摘要
通过组合属性挖掘特征和分类项目的分布是探索和理解多属性（或多元）数据的两个常见任务。到目前为止，很少有人指出将这两个任务合并为联合探索环境的可能性以及这样做的潜在好处。在本文中，我们介绍了语义，该技术通过使分析师能够在二维空间中互动地构建语义向量来实现这一目标。本质上，语义向量是原始属性的线性组合。它可用于表示和解释减少空间的局部（异常值，群集）或全局（一般模式）所隐含的抽象概念，并用作其定义概念的排名指标。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，我们设计和实施了一个视觉分析系统，其中几种交互式组件与semanticaxis无缝配合并扩大了其处理复杂场景的能力。我们通过两种实际情况证明了系统的有效性和语义技术的有效性。
Mining the distribution of features and sorting items by combined attributes are two common tasks in exploring and understanding multi-attribute (or multivariate) data. Up to now, few have pointed out the possibility of merging these two 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 two 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 two practical cases.
