Distributed and Parallel Databases

, Volume 36, Issue 2, pp 369–397 | Cite as

How to exploit high performance computing in population-based metaheuristics for solving association rule mining problem

  • Youcef Djenouri
  • Djamel Djenouri
  • Zineb Habbas
  • Asma Belhadi


The application of population-based metaheuristics approaches to the association rules mining problem is explored in this paper. The combination of GPU and cluster-based parallel computing techniques is investigated for the purpose of accelerating the process of extracting the correlations between items in sizeable data instances. We propose four parallel-based approaches that benefit from the cluster intensive computing in the generation process and the massively GPU threading. This is by evaluating the association rules in parallel on GPU. To validate the proposed approaches, the most used population-based metaheuristics (GA, PSO, and BSO) have been executed on a cluster of GPUs to solve benchmarks of large and big ARM instances. We used Intel Xeon 64bit quad-core processor E5520 coupled to an Nvidia Tesla C2075 GPU device. The results show that the BSO outperforms GA and PSO. They also show that the proposed solution outperforms the HPC-based ARM approaches when exploring Webdocs instance (the largest instance existing on the web). To our knowledge, this is the first work that explores the combination of GPU and cluster-based parallel computing with the population-based metaheuristics in association rule mining.


Cluster computing GPU computing Big data Association rule mining Population-based metaheuristics 


  1. 1.
    Olafsson, S., Li, X., Wu, S.: Operations research and data mining. Eur. J. Oper. Res. 187(3), 1429–1448 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Djenouri, Y., Habbas, Z., Djenouri, D.: Data mining-based decomposition for solving the MAXSAT problem: toward a new approach. IEEE Intell. Syst. 32(4), 48–58 (2017)CrossRefGoogle Scholar
  3. 3.
    Martnez-Ballesteros, M., Nepomuceno-Chamorro, I.A., Riquelme, J.C.: Discovering gene association networks by multi-objective evolutionary quantitative association rules. J. Comput. Syst. Sci. 80(1), 118–136 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Liu, K., Hogan, W.R., Crowley, R.S.: Natural language processing methods and systems for biomedical ontology learning. J. Biomed. Inform. 44(1), 163–179 (2011)CrossRefGoogle Scholar
  5. 5.
    Boukerche, A., Samarah, S.: A novel algorithm for mining association rules in wireless ad hoc sensor networks. IEEE Trans. Parallel Distrib. Syst. 19(7), 865–877 (2008)CrossRefGoogle Scholar
  6. 6.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)Google Scholar
  7. 7.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 1–12 (2000)Google Scholar
  8. 8.
    Zhou, X., Huang, Y.: An improved parallel association rules algorithm based on MapReduce framework for big data. In: 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 284–288. IEEE (2014, August)Google Scholar
  9. 9.
    Ravi, V.T., Agrawal, G.: Performance issues in parallelizing data-intensive applications on a multi-core cluster. In: Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, pp. 308–315. IEEE Computer Society (2009, May)Google Scholar
  10. 10.
    Cryans, J.D., Rattich, S., Champagne, R.: Adaptation of APriori to MapReduce to build a warehouse of relations between named entities across the web. In: 2010 Second International Conference on Advances in Databases Knowledge and Data Applications (DBKDA), pp. 185–189. IEEE (2010, April)Google Scholar
  11. 11.
    Jiang, W., Ravi, V.T., Agrawal, G.: A Map-Reduce system with an alternate API for multi-core environments. In: Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp. 84–93. IEEE Computer Society (2010, May)Google Scholar
  12. 12.
    Li, H., Wang, Y., Zhang, D., Zhang, M., Chang, E.Y.: Pfp: parallel fp-growth for query recommendation. In: Proceedings of the 2008 ACM conference on Recommender systems, pp. 107–114. ACM (2008, October)Google Scholar
  13. 13.
    Zhou, J., Yu, K.-M., Wu, B.-C.: Parallel frequent patterns mining algorithm on GPU. In: 2010 IEEE International Conference on Systems Man and Cybernetics (SMC). IEEE (2010)Google Scholar
  14. 14.
    Djenouri, Y., Bendjoudi, A., Mehdi, M., Nouali-Taboudjemat, N., Habbas, Z.: GPU-based bees swarm optimization for association rules mining. J. Supercomput. 71(4), 1318–1344 (2015)CrossRefGoogle Scholar
  15. 15.
    Cano, A., Luna, J.M., Ventura, S.: High performance evaluation of evolutionary-mined association rules on GPUs. J. Supercomput. 66(3), 1438–1461 (2013)CrossRefGoogle Scholar
  16. 16.
    Djenouri, Y., Comuzzi, M.: Combining Apriori heuristic and bio-inspired algorithms for solving the frequent itemsets mining problem. Inf. Sci. 420, 1–15 (2017)CrossRefGoogle Scholar
  17. 17.
    Kuo, R.J., Chao, C.M., Chiu, Y.T.: Application of particle swarm optimization to association rule mining. Appl. Soft Comput. 11(1), 326–336 (2011)CrossRefGoogle Scholar
  18. 18.
    Djenouri, Y., Drias, H., Habbas, Z.: Bees swarm optimisation using multiple strategies for association rule mining. Int. J. Bio-Inspir. Comput. 6(4), 239–249 (2014)CrossRefGoogle Scholar
  19. 19.
    Mata, J., Alvarez, J., Riquelme, J.: An evolutionary algorithm to discover numeric association rules. In: Proceedings of the ACM Symposium on Applied Computing SAC, pp. 590–594 (2002)Google Scholar
  20. 20.
    Romero, C., Zafra, A., Luna, J.M., Ventura, S.: Association rule mining using genetic programming to provide feedback to instructors from multiple-choice quiz data. Expert Syst. 30(2), 162–172 (2013)CrossRefGoogle Scholar
  21. 21.
    Djenouri, Y., Comuzzi, M.: GA-Apriori: Combining Apriori heuristic and genetic algorithms for solving the frequent itemsets mining problem. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 138–148. Springer, Cham (2017, May)Google Scholar
  22. 22.
    Martinez-Ballesteros, M., Bacardit, J., Troncoso, A., Riquelme, J.C.: Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets. Integr. Comput.-Aided Eng. 22(1), 21–39 (2015)Google Scholar
  23. 23.
    Wang, B., Merrick, K.E., Abbass, H.A.: Co-operative coevolutionary neural networks for mining functional association rules. IEEE Trans. Neural Netw. Learn. Syst. 28(6), 1331–1344 (2017)CrossRefGoogle Scholar
  24. 24.
    Fan, Z., Qiu, F., Kaufman, A., Yoakum-Stover, S.: GPU cluster for high performance computing. In: Proceedings of the 2004 ACM/IEEE conference on Supercomputing, p. 47. IEEE Computer Society (2004, November)Google Scholar
  25. 25.
    Sarath, K.N.V.D., Ravi, V.: Association rule mining using binary particle swarm optimization. Eng. Appl. Artif. Intell. 26(8), 1832–1840 (2013)CrossRefGoogle Scholar
  26. 26.
    Beiranvand, V., Mobasher-Kashani, M., Bakar, A.A.: Multi-objective PSO algorithm for mining numerical association rules without a priori discretization. Expert Syst. Appl. 41(9), 4259–4273 (2014)CrossRefGoogle Scholar
  27. 27.
    Agrawal, J., Agrawal, S., Singhai, A., Sharma, S.: SET-PSO-based approach for mining positive and negative association rules. Knowl. Inf. Syst. 45(2), 453–471 (2015)CrossRefGoogle Scholar
  28. 28.
    Djenouri, Y., Drias, H., Habbas, Z., Mosteghanemi, H.: Bees swarm optimization for web association rule mining. In: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 3, pp. 142–146). IEEE (2012, December)Google Scholar
  29. 29.
    Djenouri, Y., Drias, H., Chemchem, A.: A hybrid bees swarm optimization and tabu search algorithm for association rule mining. In: 2013 World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 120–125. IEEE (2013, August)Google Scholar
  30. 30.
    Djenouri, Y., Drias, H., Habbas, Z.: Hybrid intelligent method for association rules mining using multiple strategies. Int. J. Appl. Metaheuristic Comput. (IJAMC) 5(1), 46–64 (2014)CrossRefGoogle Scholar
  31. 31.
    Fang, W. et al.: Frequent itemset mining on graphics processors. In: Proceedings of the fifth international workshop on data management on new hardware. ACM (2009)Google Scholar
  32. 32.
    Adil, S.H., Qamar, S.: Implementation of association rule mining using CUDA. In: International Conference on Emerging Technologies, 2009. ICET 2009. IEEE (2009)Google Scholar
  33. 33.
    Silvestri, C., Orlando, S.: gpudci: exploiting gpus in frequent itemset mining. In: 2012 20th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). IEEE (2012)Google Scholar
  34. 34.
    Orlando, S. et al.: Adaptive and resource-aware mining of frequent sets. In: 2002 IEEE International Conference on Data Mining, 2002. ICDM 2003. Proceedings. IEEE (2002)Google Scholar
  35. 35.
    Zhang, F., Zhang, Y., Bakos, J.: Gpapriori: Gpu-accelerated frequent itemset mining. In: 2011 IEEE International Conference on Cluster Computing (CLUSTER). IEEE (2011)Google Scholar
  36. 36.
    Djenouri, Y., Bendjoudi, A., Mehdi, M., Habbas, Z.: Reducing thread divergence in GPU-based bees swarm optimization applied to association rule mining. Pract. Exp. Concurr. Comput. 29(9) (2016)Google Scholar
  37. 37.
    Yoo, J.S., Boulware, D.: A framework of spatial co-location mining on MapReduce. In: 2013 IEEE International Conference on Big Data, pp. 44–44. IEEE (2013, October)Google Scholar
  38. 38.
    Ding, Q., Ding, Q., Perrizo, W.: PARMAn efficient algorithm to mine association rules from spatial data. IEEE Trans. Syst. Man Cybern. Part B 38(6), 1513–1524 (2008)CrossRefGoogle Scholar
  39. 39.
    Taleb, A., Yahya, A., Taleb, N.: Parallel genetic algorithm model to extract association rules. In: DBKDA 2013, The Fifth International Conference on Advances in Databases, Knowledge, and Data Applications, pp. 56–64 (2013, January)Google Scholar
  40. 40.
    Bull, L., Studley, M., Bagnall, A., Whittley, I.: Learning classifier system ensembles with rule-sharing. IEEE Trans. Evolut. Comput. 11(4), 496–502 (2007)CrossRefGoogle Scholar
  41. 41.
    Chen, Y., Li, F., Fan, J.: Mining association rules in big data with NGEP. Clust. Comput. 18(2), 577–585 (2015)CrossRefGoogle Scholar
  42. 42.
    Sousa, T., Silva, A., Neves, A.: Particle swarm based data mining algorithms for classification tasks. Parallel Comput. 30(5), 767–783 (2004)CrossRefGoogle Scholar
  43. 43.
    Djenouri, Y., Bendjoudi, A., Djenouri, D., Habbas, Z.: Parallel BSO algorithm for association rules mining using master/worker paradigm. In: International Conference on Parallel Processing and Applied Mathematics, pp. 258–268. Springer, New York (2015, September)Google Scholar
  44. 44.
    Orgerie, A.C., Assuncao, M.D.D., Lefevre, L.: A survey on techniques for improving the energy efficiency of large-scale distributed systems. ACM Comput. Surv. (CSUR) 46(4), 47 (2014)CrossRefGoogle Scholar
  45. 45.
    Lucchese, C., Orlando, S., Perego, R., Silvestri, F.: WebDocs: a real-life huge transactional dataset. In: Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementation (2004, November)Google Scholar
  46. 46.
    Guvenir, H. Altay, Uysal, I.: Bilkent university function approximation repository. Accessed 12 Mar 2012 (2000)
  47. 47.
    Kaur, B., Jindal, S.: Content based image retrieval with graphical processing unit. In: Int. Conf. on Recent Trends in Information, Telecommunication and Computing, ITC (2014, April)Google Scholar
  48. 48.
    Nobile, M.S., Cazzaniga, P., Besozzi, D., Mauri, G.: GPU-accelerated simulations of mass-action kinetics models with cupSODA. J. Supercomput. 69(1), 17–24 (2014)CrossRefGoogle Scholar
  49. 49.
    Parthasarathy, S., Zaki, M.J., Ogihara, M., Li, W.: Parallel data mining for association rules on shared-memory systems. Knowl. Inf. Syst. 3(1), 1–29 (2001)CrossRefzbMATHGoogle Scholar
  50. 50.
    Agrawal, R., Shafer, J.C.: Parallel mining of association rules. IEEE Trans. Knowl. Data Eng. 8(6), 962–969 (1996)CrossRefGoogle Scholar
  51. 51.
    Ryoo, S., Rodrigues, C.I., Stone, S.S., Stratton, J.A., Ueng, S.Z., Baghsorkhi, S.S., Wen-mei, W.H.: Program optimization carving for GPU computing. J. Parallel Distrib. Comput. 68(10), 1389–1401 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Youcef Djenouri
    • 1
  • Djamel Djenouri
    • 2
  • Zineb Habbas
    • 3
  • Asma Belhadi
    • 4
  1. 1.IMMADASouthern Denmark UniversityOdenseDenmark
  2. 2.CERIST Center ResearchAlgiersAlgeria
  3. 3.Lorraine UniversityMetzFrance
  4. 4.RIMA LabUSTHBAlgiersAlgeria

Personalised recommendations