Observation-Level Interaction with Clustering and Dimension Reduction Algorithms
Observation-Level Interaction with Clustering and Dimension Reduction Algorithms
复制标题
观察级与聚类和降维算法的交互
DOI：
10.1145/3077257.3077259
复制
发表时间：
2017
期刊：
影响因子：
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通讯作者：
Chris North
中科院分区：
文献类型：
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作者：
John E. Wenskovitch;Chris North
观察级交互(OLI)是一种依赖于对数据的交互语义探索的感觉制造技术。通过操作可视化内的数据项，用户向将多维数据投影成有意义的二维表示的底层数学模型提供反馈。在这项工作中，我们提出、实现和评估了一个OLI模型，该模型显式地定义了该数据投影中的簇。这些集群提供了可针对其操纵数据值的目标。结果是一个协作框架，其中数据的布局影响集群，而用户驱动的与集群的交互影响数据点的布局。此外，该模型通过提供一组清晰的集群来解决OLI“关于什么”的问题，交互目标是根据这些集群来判断和计算的。
Observation-Level Interaction (OLI) is a sensemaking technique relying upon the interactive semantic exploration of data. By manipulating data items within a visualization, users provide feedback to an underlying mathematical model that projects multidimensional data into a meaningful two-dimensional representation. In this work, we propose, implement, and evaluate an OLI model which explicitly defines clusters within this data projection. These clusters provide targets against which data values can be manipulated. The result is a cooperative framework in which the layout of the data affects the clusters, while user-driven interactions with the clusters affect the layout of the data points. Additionally, this model addresses the OLI "with respect to what" problem by providing a clear set of clusters against which interaction targets are judged and computed.