Interactive Visual Exploration of Local Patterns in Large Scatterplot Spaces
Interactive Visual Exploration of Local Patterns in Large Scatterplot Spaces
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DOI：
10.1111/cgf.13404
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发表时间：
2018-06
影响因子：
2.5
通讯作者：
Mohammad Chegini;Lin Shao;Robert Gregor;D. Lehmann;K. Andrews;Tobias Schreck
中科院分区：
文献类型：
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作者：
Mohammad Chegini;Lin Shao;Robert Gregor;D. Lehmann;K. Andrews;Tobias Schreck
分析师经常使用散点图矩阵（SPLOM）等可视化技术来探索多变量数据集。SPLOM的散点图可以帮助识别和比较二维全局模式。然而，可能仅存在于记录子集内的局部模式通常更难识别，并且可能在SPLOM中的较大图集中被忽视。本文探讨了局部模式的概念，并提出了一种新的方法来可视化地选择，搜索和比较多变量数据集中的局部模式。基于模型和基于形状的模式描述符用于自动比较散点图中的局部区域，以帮助发现相似的局部模式。提供了评估局部模式之间的相似性水平并有效地对相似模式进行排名的机制。此外，相关性反馈模块用于向用户建议潜在相关的局部模式。该方法已在一个交互式工具中实现，并通过两个真实的世界数据集和用例进行了演示。它支持发现潜在有用的信息，如聚类、变量之间的函数依赖关系以及数据记录和维度子集中的统计关系。
Analysts often use visualisation techniques like a scatterplot matrix (SPLOM) to explore multivariate datasets. The scatterplots of a SPLOM can help to identify and compare two‐dimensional global patterns. However, local patterns which might only exist within subsets of records are typically much harder to identify and may go unnoticed among larger sets of plots in a SPLOM. This paper explores the notion of local patterns and presents a novel approach to visually select, search for, and compare local patterns in a multivariate dataset. Model‐based and shape‐based pattern descriptors are used to automatically compare local regions in scatterplots to assist in the discovery of similar local patterns. Mechanisms are provided to assess the level of similarity between local patterns and to rank similar patterns effectively. Moreover, a relevance feedback module is used to suggest potentially relevant local patterns to the user. The approach has been implemented in an interactive tool and demonstrated with two real‐world datasets and use cases. It supports the discovery of potentially useful information such as clusters, functional dependencies between variables, and statistical relationships in subsets of data records and dimensions.