A Parallel Algorithm for Building iCPI-trees-Reference-Cited by-同舟云学术

A Parallel Algorithm for Building iCPI-trees

Published:2014 Issue: Volume: Page:276-289
ISSN:0302-9743
Container-title:Advances in Databases and Information Systems
language:
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Author:

Andrzejewski Witold,Boinski Pawel

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-10933-6_21

Reference19 articles.

1. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994)

2. Alcantara, D.A.F.: Efficient Hash Tables on the GPU. PhD thesis, University of California, Davis (2011)

3. Lecture Notes in Computer Science;W. Andrzejewski,2013

4. Bell, N., Hoberock, J.: GPU Computing Gems: Jade edition, chapter Thrust: A Productivity-Oriented Library for CUDA, pp. 359–371. Morgan-Kauffman (2011)

5. Lecture Notes in Computer Science;P. Boinski,2012

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