A Study of Hierarchical Correlation Clustering for Scientific Volume Data [chapter]

Yi Gu, Chaoli Wang
2010 Lecture Notes in Computer Science  
Correlation study is at the heart of time-varying multivariate volume data analysis and visualization. In this paper, we study hierarchical clustering of volumetric samples based on the similarity of their correlation relation. Samples are selected from a time-varying multivariate climate data set according to knowledge provided by the domain experts. We present three different hierarchical clustering methods based on quality threshold, k-means, and random walks, to investigate the correlation
more » ... elation with varying levels of detail. In conjunction with qualitative clustering results integrated with volume rendering, we leverage parallel coordinates to show quantitative correlation information for a complete visualization. We also evaluate the three hierarchical clustering methods in terms of quality and performance.
doi:10.1007/978-3-642-17277-9_45 fatcat:jrvhxprymzfnrf5ymt47eshb7q