Out-of-core clustering of volumetric datasets  

作　　者：GRANBERG Carl J. 

机构地区：[1]Department of Computing, Curtin University of Technology, Perth, Australia

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》2006年第7期1134-1140,共7页浙江大学学报（英文版）A辑（应用物理与工程）

摘　　要：In this paper we present a novel method for dividing and clustering large volumetric scalar out-of-core datasets. This work is based on the Ordered Cluster Binary Tree (OCBT) structure created using a top-down or divisive clustering method. The OCBT structure allows fast and efficient sub volume queries to be made in combination with level of detail (LOD) queries of the tree. The initial partitioning of the large out-of-core dataset is done by using non-axis aligned planes calculated using Principal Component Analysis (PCA). A hybrid OCBT structure is also proposed where an in-core cluster binary tree is combined with a large out-of-core file.In this paper we present a novel method for dividing and clustering large volumetric scalar out-of-core datasets. This work is based on the Ordered Cluster Binary Tree （OCBT） structure created using a top-down or divisive clustering method. The OCBT structure allows fast and efficient sub volume queries to be made in combination with level of detail （LOD） queries of the tree. The initial partitioning of the large out-of-core dataset is done by using non-axis aligned planes calculated using Principal Component Analysis （PCA）. A hybrid OCBT structure is also proposed where an in-core cluster binary tree is combined with a large out-of-core file.

关 键 词：Out-of-core clustering Hybrid rendering Scientific visualization 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]