| 摘要: |
| 聚类是研究空间多变量数据的重要工具之一.但是自动聚类算法通常需要用户预设参数然后生成结果,缺乏一种有效的交互机制将用户介入到聚类的过程,使之动态改变参数并对结果进行调整和评估.为此提出一种面向空间多变量数据聚类的可视分析流程,首先运用自动聚类算法对原始三维空间进行聚类,针对三维空间不易交互的缺陷将数据点投影到二维平面进行交互选择和可视编码,设置多种视图使用户实时而全面地理解数据分布和模式,交互地修正聚类结果,并根据一些编码的统计信息来判断结果的合理性和正确性.整个流程是渐进式的,即用户通过迭代逐步细化结果,最终抽取兴趣域.案例分析表明,新的可视分析流程能够有效地提高空间自动聚类算法的精度,也极大地缩短了用户交互的时间. |
| 关键词: 空间多变量数据场 可视化 交互 空间聚类 |
| DOI： |
| 分类号: |
| 基金项目:国家自然科学基金(81172124,61232012);国家高技术研究发展计划(863)(2012AA12090) |
|
| Visual Analysis Approach for Clustering Multivariate Spatial Data |
|
WU Fei-Ran1,2, CHEN Hai-Dong1,2, HUANG Jin1,2, CHEN Wei1,2
|
|
1.State Key Laboratory of CAD &2.CG (Zhejiang University), Hangzhou 310058, China
|
| Abstract: |
| Clustering is one of important tools to study the multivariate spatial data. However, automatic clustering algorithms require the user to finely modulate parameters, imposing the need for an effective mechanism to manipulate the clustering process by dynamically changing the parameters and evaluating the results. This paper proposes a novel visual analysis approach for clustering multivariate spatial data. First, the underlying dataset is clustered in 3D using an automatic clustering algorithm. Second, the result is examined and refined on its 2D projection by leveraging a suite of visualization and analysis toolkits. The user is allowed to intuitively verify and adjust the clusters by referring to the visual encoding and visual patterns. The entire process is progressively performed in a raw-to-fine fashion. The case study on a high-dimensional symmetric tensor field verifies the effectiveness and robustness of the proposed approach. |
| Key words: multivariable visualization interaction spatial clustering |
