A study of hierarchical correlation clustering for scientific volume data
Department of 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 relation 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.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A study of hierarchical correlation clustering for scientific volume data.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
6455 LNCS(PART 3), 437-446.
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/4182