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Exploiting parallel graphics processing units to improve association rule mining in transactional databases using butterfly optimization algorithm

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Abstract

Extracting association rules from huge amounts of data is an essential method in data mining that can provide valuable knowledge from datasets for various applications. In addition to a variety of traditional and parallel association rule mining (ARM) methods, some studies have also been represented in the literature to extract association rules from datasets using multi-core processors combining GPU-CPU (graphics processing unit-central processing unit) and FPGA-CPU (field programmable gate array-central processing unit). These parallel methods have been utilized to speed up the process of ARM, including its major phase, frequent itemset mining (FIM). The use of multiple GPUs for ARM and FIM has been usually in the cluster form, and the simultaneous use of multiple GPUs on a single system has not been extensively used to extract the set of association rules. Due to the huge volume of big data, finding more efficient and faster ARM and FIM methods is still of interest to researchers. In this paper, the butterfly optimization algorithm (BOA), which has an acceptable accuracy and speed in solving optimization problems, is used for ARM. In this study, an efficient platform including a single CPU and three parallel GPUs are employed to parallelize ARM using BOA. The main feature of the proposed method is the use of parallel GPUs on a single computer, to speed up the process, and the use of the CPU as a synchronizer. Since the GPUs reduce the runtime by executing similar duplicate structures, the proposed model speeds up the mining process and prevents computing overload on the CPU. All three phases of the algorithm are implemented on the parallel graphics cards to increase the performance. The evaluation of the proposed method and its comparison with the BSO- and GBSO-Miner methods show its better performance in terms of accuracy and execution time.

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References

  1. Agrawal, R., Imieliński, T., Swami, A. (1993) Mining association rules between sets of items in big databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data (pp. 207–216).

  2. Agrawal, R., Srikant, R. (1994). Fast algorithms for mining association rules. In Proc. 20th Int. Conf. very big data bases, VLDB, 1215, 487-499

  3. Houtsma, M., Swami, A. (1995) Set-oriented mining for association rules in relational databases. In Proceedings of the eleventh international conference on data engineering (pp. 25–33). IEEE.

  4. Qodmanan, H.R., Nasiri, M., Minaei-Bidgoli, B.: Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence. Expert Syst. Appl. 38(1), 288–298 (2011)

    Article  Google Scholar 

  5. Park, J., Chen, M. S., Yu, P. S. (1995). An effective hash based algorithm for mining association rules. In: SIGMOD’95, p.175–186.

  6. Toivonen, H. (1996). Sampling large databases for association rules. In: Proceeding of the 1996 International Conference on very Large Data Bases (VLDB’96), p.134–145.

  7. Han, J., Pei, J., Yin, Y. (2000). Mining frequent patterns without candidate generation. In: Proceeding of the 2000 ACM-SIGMOD International Conference on Management of Data (SIGMOD’00), p.1–12.

  8. Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Disc. 8(1), 53–87 (2004)

    Article  MathSciNet  Google Scholar 

  9. Deng, H., Lv, S.L.: Fast mining frequent itemsets using Nodesets. Expert Syst. Appl. 41(10), 4505–4512 (2014)

    Article  Google Scholar 

  10. Deng, Z.H., Wang, Z.H., Jiang, J.J.: A new algorithm for fast mining frequent itemsets using n-lists. Sci. China Inf. Sci. 55(9), 2008–2030 (2012)

    Article  MathSciNet  Google Scholar 

  11. Deng, H., Lv, S.L.: PrePost+: an efficient N-lists-based algorithm for mining frequent itemsets via children-parent equivalence pruning. Expert Syst. Appl. 42(13), 5424–5432 (2015)

    Article  Google Scholar 

  12. Deng, Z.H.: DiffNodesets: an efficient structure for fast mining frequent itemsets. Appl. Soft Comput. 41, 214–223 (2016)

    Article  Google Scholar 

  13. Djenouri, Y., Comuzzi, M.: Combining apriori heuristic and bio-inspired algorithms for solving the frequent itemsets mining problem. Inf. Sci. 420, 1–15 (2017)

    Article  Google Scholar 

  14. Agrawal, M., Mishra, M., Kushwah, S.P.S.: Association rules optimization using particle swarm optimization algorithm with mutation. Int. J. Soft Comput. Eng. (IJSCE) 5(1), 141–144 (2015)

    Google Scholar 

  15. Djenouri, Y., Djenouri, D., Belhadi, A., Fournier-Viger, P., Lin, J.C.-W.: A new framework for metaheuristic-based frequent itemset mining. Appl. Intell. 48, 4775–4791 (2018)

    Article  Google Scholar 

  16. Agrawal, J., Agrawal, S., Singhai, A., Sharma, S.: (2015) SET-PSO-based approach for mining positive and negative association rules. Knowl. Inf. Syst. 45, 453–471 (2015)

    Article  Google Scholar 

  17. Yadav, A., Vishwakarma, D.K.: A comparative study on bio-inspired algorithms for sentiment analysis. Cluster Comput. 23, 2969–2989 (2020)

    Article  Google Scholar 

  18. Wang, T., Manogaran, G., Wang, M.: Framework for social tag recommendation using lion optimization algorithm and collaborative filtering techniques. Cluster Comput. 23, 2009–2019 (2020)

    Article  Google Scholar 

  19. Abualigah, L., Dulaimi, A.J.: A novel feature selection method for data mining tasks using hybrid Sine Cosine algorithm and genetic algorithm. Cluster Comput. (2021). https://doi.org/10.1007/s10586-021-03254-y

    Article  Google Scholar 

  20. Sankalap, A., Singh, S.: Butterfly optimization algorithm: a novel approach for global optimization. Soft. Comput. 23(3), 715–734 (2019)

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Agapito, G., Guzzi, P.H., Cannataro, M.: Parallel mining of association rules from genomics data. Appl. Math. Comput. 350, 434–446 (2019)

    MathSciNet  MATH  Google Scholar 

  23. Chen, M., Gao, X. Li, H. (2009) An efficient parallel FP-Growth algorithm. In 2009 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (pp. 283–286). IEEE.

  24. Sohrabi, M.K., Taheri, N.: A haoop-based parallel mining of frequent itemsets using N-Lists. J. Chin. Inst. Eng. 41(1), 229–238 (2018)

    Article  Google Scholar 

  25. Sohrabi, M.K.: A gossip-based information fusion protocol for distributed frequent itemset mining. Enterp. Inf. Syst. 12(6), 674–694 (2018)

    Article  Google Scholar 

  26. Huang, C.H., Leu, Y.: Multi-level dataset decomposition for parallel frequent itemset mining on a cluster of personal computers. Cluster Comput. 22, 2851–2863 (2019)

    Article  Google Scholar 

  27. Saleti, S., Subramanyam, R.B.V.: A novel bit vector product algorithm for mining frequent itemsets from large datasets using MapReduce framework. Cluster Comput. 21, 1365–1380 (2018)

    Article  Google Scholar 

  28. Chon, K.W., Kim, M.S.: BIGMiner: a fast and scalable distributed frequent pattern miner for big data. Cluster Comput. 21, 1507–1520 (2018)

    Article  Google Scholar 

  29. Zhang, F., Zhang, Y., Bakos, J.: “GPApriori: GPU-accelerated frequent itemset mining.” IEEE Int. Conf. Cluster Comput. 2011, 590–594 (2011). https://doi.org/10.1109/CLUSTER.2011.61

    Article  Google Scholar 

  30. Chon, K.-W., Hwang, S.-H., Kim, M.-S.: GMiner: a fast GPU-based frequent itemset mining method for large-scale data. Inf. Sci. 439–440, 19–38 (2018)

    Article  MathSciNet  Google Scholar 

  31. Djenouri, Y., Djenouri, D., Belhadi, A., Cano, A.: Exploiting GPU and cluster parallelism in single scan frequent itemset mining. Inf. Sci. 496, 363–377 (2019)

    Article  Google Scholar 

  32. Djenouri, Y., Djenouri, D., Belhadi, A., Cano, A., Fournier-Viger, P., Lin, J.C.-W., Bendjoudi, A.: Exploiting GPU parallelism in improving bees swarm optimization for mining big transactional databases. Inf. Sci. 496, 326–342 (2019)

    Article  Google Scholar 

  33. Djenouri, Y., Bendjoudi, A., Mehdi, M., Nouali-Taboujemat, N., Habbas, Z.: GPU-based bees swarm optimization for association rules mining. J Supercomput 71, 1318–1344 (2015)

    Article  Google Scholar 

  34. Djenouri, Y., Belhadi, A., Fournier-Viger, P., Fujita, H.: Mining diversified association rules in big datasets: a cluster/GPU/genetic approach. Inf. Sci. 459, 117–134 (2018)

    Article  MathSciNet  Google Scholar 

  35. Djenouri, Y., Djenouri, D., Habbas, Z., Belhadi, A.: How to exploit high performance computing in population-based metaheuristics for solving association rule mining problem. Distrib. Parallel Databases 36(2), 369–397 (2018)

    Article  Google Scholar 

  36. Sarath, K.N.V.D., Ravi, V.: Association rule mining using binary particle swarm optimization. Eng. Appl. Artif. Intell. 26(8), 1832–1840 (2013)

    Article  Google Scholar 

  37. Fung, K.Y., Kwong, C.K., Siu, K.W., Yu, K.M.: A multi-objective genetic algorithm approach to rule mining for affective product design. Expert Syst. Appl. 39(8), 7411–7419 (2012)

    Article  Google Scholar 

  38. Heraguemi, K.E., Kamel, N., Drias, H.: Multi-swarm bat algorithm for association rule mining using multiple cooperative strategies. Appl. Intell. 45(4), 1021–1033 (2016)

    Article  Google Scholar 

  39. Agarwal, A., Nanavati, N.: Association rule mining using hybrid GA-PSO for multi-objective optimisation. In 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, pp. 1–7 (2016). https://doi.org/10.1109/ICCIC.2016.7919571

  40. Djenouri, Y., Drias, H., Habbas, Z.: Bees swarm optimisation using multiple strategies for association rule mining. Int. J. Bio-Inspired Comput. 6(4), 239–249 (2014)

    Article  Google Scholar 

  41. Telikani, A., Gandomi, A.H., Shahbahrami, A.: A survey of evolutionary computation for association rule mining. Inf. Sci. 524, 318–352 (2020)

    Article  MathSciNet  Google Scholar 

  42. Djenouri, Y., Djenouri, D., Habbas, Z.: Intelligent mapping between GPU and cluster computing for discovering big association rules. Appl. Soft Comput. 65, 387–399 (2018)

    Article  Google Scholar 

  43. Zhang, F., Zhang, Y., Bakos, J.D.: Accelerating frequent itemset mining on graphics processing units. J Supercomput 66, 94–117 (2013)

    Article  Google Scholar 

  44. Jurczuk, K., Czajkowski, M., Kretowski, M.: Multi-GPU approach to global induction of classification trees for large-scale data mining. Appl. Intell. (2021). https://doi.org/10.1007/s10489-020-01952-5

    Article  Google Scholar 

  45. Andrzejewski, W., Boinski, P.: Efficient spatial co-location pattern mining on multiple GPUs. Expert Syst. Appl. 93, 465–483 (2018)

    Article  Google Scholar 

  46. https://archive.ics.uci.edu/ml/datasets.php,. Accessed July 2020

  47. http://fimi.uantwerpen.be/data. Accessed July 2020

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Authors and Affiliations

  1. Department of Computer Engineering, Semnan Branch, Islamic Azad University, Semnan, Iran

    Ali Abbas Zoraghchian, Mohammad Karim Sohrabi & Farzin Yaghmaee

  2. Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran

    Farzin Yaghmaee

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  1. Ali Abbas Zoraghchian
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  2. Mohammad Karim Sohrabi
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  3. Farzin Yaghmaee
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Correspondence to Mohammad Karim Sohrabi.

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Zoraghchian, A.A., Sohrabi, M.K. & Yaghmaee, F. Exploiting parallel graphics processing units to improve association rule mining in transactional databases using butterfly optimization algorithm. Cluster Comput 24, 3767–3778 (2021). https://doi.org/10.1007/s10586-021-03369-2

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