High performance evaluation of evolutionary-mined association rules on GPUs-Reference-Cited by-同舟云学术

High performance evaluation of evolutionary-mined association rules on GPUs

Published:2013-04-26 Issue:3 Volume:66 Page:1438-1461
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Cano Alberto,Luna José María,Ventura Sebastián

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Link

http://link.springer.com/content/pdf/10.1007/s11227-013-0937-4.pdf

Reference48 articles.

1. NVIDIA CUDA Programming, Best practices guide, http://www.nvidia.com/cuda (2013)

2. Agrawal R, Shafer JC (1996) Parallel mining of association rules. IEEE Trans Knowl Data Eng 8(6):962–969

3. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of 20th international conference on very large data bases, pp 487–499

4. Alatas B, Akin E (2006) An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules. Soft Comput 10:230–237

5. Alba E, Tomassini M (2002) Parallelism and evolutionary algorithms. IEEE Trans Evol Comput 6(5):443–462

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