CFP last date
20 March 2024
Reseach Article

Mining Frequent Patterns with Optimized Candidate Representation on Graphics Processor

by Dharmesh Bhalodia, Chhaya Patel
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 105 - Number 7
Year of Publication: 2014
Authors: Dharmesh Bhalodia, Chhaya Patel

Dharmesh Bhalodia, Chhaya Patel . Mining Frequent Patterns with Optimized Candidate Representation on Graphics Processor. International Journal of Computer Applications. 105, 7 ( November 2014), 1-8. DOI=10.5120/18386-9634

@article{ 10.5120/18386-9634,
author = { Dharmesh Bhalodia, Chhaya Patel },
title = { Mining Frequent Patterns with Optimized Candidate Representation on Graphics Processor },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 105 },
number = { 7 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { },
doi = { 10.5120/18386-9634 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:37:03.489857+05:30
%A Dharmesh Bhalodia
%A Chhaya Patel
%T Mining Frequent Patterns with Optimized Candidate Representation on Graphics Processor
%J International Journal of Computer Applications
%@ 0975-8887
%V 105
%N 7
%P 1-8
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

Frequent itemset mining algorithms mine subsets of items that appear frequently in a collection of sets. FIM is a key investigation in numerous data mining applications, and the FIM tools are among the most computationally demanding in data mining. In this research paper we present a new approach to represent candidate in parallel Frequent Itemset Mining algorithm. Our new approach is extension of GPApriori, a GP-GPU version of FIM. This implementation is optimized to achieve high performance on a heterogeneous platform consisting of a shared memory multiprocessor and multiple cores NVIDIA based Graphics Processing Unit (GPU) coprocessor. An experiments compared with the GPApriori on NVIDIA Kepler GPUs and observed 1. 5X to 2X required less memory and significant improvements in time relative to GPApriori.

  1. R. Agrawal, T. Imielinski, and A. Swami, "Mining association rules between set of items in large databases," ACM SIGMOD International conference on management of data, pp. 207–216, May 1993.
  2. NVIDIA Kepler GK110 Architecture Whitepaper(White Paper), january 2013. [Online]. Available: http://www. nvidia. com/content/PDF/kepler/ NVIDIA-Kepler-GK110-Architecture-Whitepaper. pdf
  3. NVIDIA CUDA TOOLKIT VERSION 6. 0 RELEASE NOTE, Feb. 2014. [Online]. Available: http://developer. download. nvidia. com/compute/ cuda/6 0/rc/docs/CUDA Toolkit Release Notes. pdf
  4. M. J. Zaki, "Parallel and distributed association mining: A survey," IEEE Concurrency, vol. 7, no. 4, pp. 14–25, Octomber 1999.
  5. J. P. J. Han and Y. Yin, "Mining frequent patterns without candidate generation," in SIGMOD, pp. 1–12, 2000.
  6. M. E. -H. O. R. Zaiane and P. Lu, "Fast parallel association rule mining without candidacy generation," no. 1, 2001, pp. 665–668.
  7. Y. Z. L. Liu, E. Li and Z. Tang, "Optimization of frequent itemset mining on multiple-core processor," 2007, pp. 1275–1285.
  8. S. P. D. K. A. N. Y. K. C. A. Ghoting, G. Buehrer and P. Dubey, "Cache-conscious frequent pattern mining on modern and emerging processors," The VLDB Journal, vol. 16, no. 1, pp. 77–96, 2007.
  9. P. K. M. Bhalodiya Dharmesh and P. C. , "An efficient way to find frequent pattern with dynamic programming approach," in Engineering (NUiCONE), 2013 Nirma University International Conference on, Nov 2013, pp. 1–5.
  10. X. X. B. H. Q. L. Wenbin Fang, Mian Lu, "Frequent itemset mining on graphics processors," Proceedings of the Fifth International Workshop on Data Management on New Hardware DaMoN2009, 2009.
  11. Y. Z. Fan Zhang and J. Bakos, "Gpapriori: Gpu-accelerated frequent itemset mining. " In 2011 IEEE International Conference on Cluster Computing (CLUSTER), no. 3, pp. 590–594, March 2011.
  12. P. S. Zaki MJ, "New algorithms for fast discovery of association rules. " Menlo Park: AAAI Press, 1997, pp. 283–286.
  13. P. J. Han J, "Mining frequent patterns without candidate generation: a frequent-pattern tree approach," Data Mining Knowl Discov, no. 8, pp. 53–87, 2004.
  14. Y. Z. Fan Zhang and J. Bakos, "Accelerating frequent itemset mining on graphics processing units," Journal of Supercomput, no. 66, pp. 94–117, February 2013.
  15. W. M. J. George Teodoro, Nathan Mariano and R. Ferreira, "Tree projection-based frequent itemset mining on multicore cpus and gpus. " Washington, DC, USA: IEEE Computer Society, March 2010. , no. 3.
  16. C. C. A. Ramesh C. Agarwal and V. V. V. Prasad, "A tree projection algorithm for generation of frequent item sets," Journal of Parallel Distributed Computing, no. 3, March 2001.
  17. R. Agrawal and J. Shafer, "Parallel mining of association rules," In IEEE Trans. on Knowledge and Data Engg, no. 8, pp. 962–969, 1996.
  18. F. V. C. S. Salvatore O. , Universit a Ca, "Exploiting gpus in frequent itemset mining," 20th Euromicro International Conference on Parallel, Distributed and Network-based Processing, pp. 416–425, 2012.
  19. B. Dharmesh and P. Chhaya, "Comparative study of frequent itemset mining techniques on graphics processor," International Journal of Engineering Research and Applications, vol. 4, no. 1, pp. 159–163, April 2014.
  20. CUDA C BEST PRACTICES GUIDE, July 2013. [Online]. Available: http://docs. nvidia. com/cuda/pdf/CUDA C Best Practices Guide. pdf
  21. NVIDIA CUDA compute unified device architecture programming guide, February 2014. [Online]. Available: http://docs. nvidia. com/cuda/pdf/CUDA C Programming Guide. pdf
  22. B. G, Frequent itemset mining dataset repository, 2004. [Online]. Available: http://fimi. ua. ac. be/data/
Index Terms

Computer Science
Information Sciences


Association rule mining CUDA GPU computing Frequent itemset mining Parallel computing