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Vis Inf
Vis Inf  2018, Vol. 2 Issue (4): 254-263    DOI: 10.1016/j.visinf.2018.12.005
论文     
多变量数据协同可视探索框架
Xiangyang He, Yubo Tao, Qirui Wang, Hai Lin
State Key Lab of CAD&CG, Zhejiang University, China
A co-analysis framework for exploring multivariate scientific data
Xiangyang He, Yubo Tao, Qirui Wang, Hai Lin
State Key Lab of CAD&CG, Zhejiang University, China
 全文: PDF 
摘要： 物理仿真模型可以模拟不同的物理现象生成大量的多变量数据集。不同变量在模拟过程中协同工作，因此它们之间通常有隐含的相关性。通常情况下，变量集在局部区域往往会表现出较强的相关性，因此，提取变量在不同区域的局部相关性比基于所有体素度量的全局相关性更为必要。 为了探索多变量之间的局部相关性，本文提出了一种基于双聚类的多变量数据协同探索框架，自动提取有意义的局部特征（由变量子集和体素子集构成且对应体素在对应的变量上具有相似的数值模式的集合，即bicluster），并设计多个视图探索多变量数据的局部相关性。

关键词： 多变量数据;  双聚类;  局部关系    
Abstract: In a complex multivariate data set, different features usually have diverse associations with different variables, and different variables are also associated within different regions. Thus, it is necessary to explore these associations between variables and voxels locally to better understand the underlying phenomena. In this paper, we propose a co-analysis framework based on biclusters, i.e., two subsets of variables and voxels with close scalar-value relationships, to guide the visual exploration process of multivariate data. We first extract all meaningful biclusters automatically, each of which only contains voxels with a similar scalar-value pattern over a subset of variables. These biclusters are organized according to their variable sets, and biclusters in each variable set are further grouped by a similarity metric to reduce redundancy and encourage diversity during visual exploration. Biclusters are visually represented in coordinated views to facilitate interactive exploration of multivariate data from the similarity between biclusters and the correlation of scalar values with different variables. Experiments with several representative multivariate scientific data sets demonstrate the effectiveness of our framework in exploring local relationships among variables, biclusters and scalar values in the data.
Key words: Multivariate data    Bicluster    Local association
出版日期: 2019-01-09
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Xiangyang He
Yubo Tao
Qirui Wang
Hai Lin

引用本文:

Xiangyang He, Yubo Tao, Qirui Wang, Hai Lin. A co-analysis framework for exploring multivariate scientific data. Vis Inf, 2018, 2(4): 254-263.

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http://www.zjujournals.com/vi/CN/10.1016/j.visinf.2018.12.005    或    http://www.zjujournals.com/vi/CN/Y2018/V2/I4/254

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