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Received: 2006-04-07

Revision Accepted: 2006-04-19

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Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.7 P.1134-1140


Out-of-core clustering of volumetric datasets

Author(s):  GRANBERG Carl J., LI Ling

Affiliation(s):  Department of Computing, Curtin University of Technology, Perth, Australia

Corresponding email(s):   granberg@cs.curtin.edu.au, ling@cs.curtin.edu.au

Key Words:  Out-of-core clustering, Hybrid rendering, Scientific visualization

GRANBERG Carl J., LI Ling. Out-of-core clustering of volumetric datasets[J]. Journal of Zhejiang University Science A, 2006, 7(7): 1134-1140.

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publisher="Zhejiang University Press & Springer",

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T1 - Out-of-core clustering of volumetric datasets
A1 - LI Ling
J0 - Journal of Zhejiang University Science A
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2006.A1134

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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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