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A Parallel Algorithm for Fast Mining Frequent Weighted Ultility Patterns from Dynamic Quantitative Databases

  • Nguyen Le iSPACE
  • Ham Nguyen
  • Minh Nguyen
Keywords: frequent weighted utility patterns, dynamic quantitative database, big data, parallel computation

Abstract

In recent years, the problem of mining frequent weighted utility patterns has been receiving research attention. This is a variation of the pattern mining problem. Many effective methods have been proposed to solve this problem. However, when the data is extensive and the weights of items change frequently, the algorithms take much time during the mining process. If we take advantage of the parallel computing capabilities of computing systems or multi-core processors, we can improve the mining time of algorithms. This paper presents a parallel solution operating on multi-core processors, named pdFWUNL, to exploit frequent weighted utility patterns from dynamic quantitative databases with variable item
weight. The experimental results show that the mining time of our parallel algorithm, pdFWUNL, is more efficient than the best available sequential method.

References

R. Agrawal, T. Imielinski, and A. Swami, “Mining association rules between sets of items in large databases,” vol. 22. ACM, 1993, pp. 207–216.

M. S. Khan, M. Muyeba, and F. Coenen, “A weighted utility framework for mining association rules.” IEEE, 2008, pp. 87–92.

B. Vo, B. Le, and J. J. Jung, “A tree-based approach for mining frequent weighted utility itemsets.” Springer, 2012, pp. 114–123.

N. D. Ham, V. D. Bay, N. T. H. Minh, and T.-P. Hong, “Mbis: an efficient method for mining frequent weighted utility itemsets from quantitative databases,” Journal of Computer Science and Cybernetics, vol. 31, no. 1, pp. 17–30, 2015.

H. Bui, B. Vo, and H. Nguyen, “Wun-miner: A new method for mining frequent weighted utility itemsets.” IEEE, 2016, pp. 001 365–001 370.

H. Nguyen, N. Le, H. Bui, and T. Le, “A new approach for efficiently mining frequent weighted utility patterns,” Applied Intelligence, vol. 53, no. 1, pp. 121–140, 1/2023.

H. Nguyen, N. Le, H. Bui, and T. Le, “Mining frequent weighted utility patterns with dynamic weighted

items from quantitative databases,” Applied Intelligence, 3/2023, [Online; accessed 2023-06-15]. [Online]. Available: https://link.springer.com/10.1007/s10489-023-04554-z

H. Yao, H. J. Hamilton, and C. J. Butz, “A foundational approach to mining itemset utilities from databases.” SIAM, 2004, pp. 482–486.

Y. Liu, W.-k. Liao, and A. Choudhary, “A two-phase algorithm for fast discovery of high utility itemsets.” Springer, 2005, pp. 689–695.

V. S. Tseng, B.-E. Shie, C.-W. Wu, and S. Y. Philip, “Efficient algorithms for mining high utility itemsets from transactional databases,” IEEE transactions on knowledge and data engineering, vol. 25, no. 8, pp. 1772–1786, 2012.

M. Liu and J. Qu, “Mining high utility itemsets without candidate generation,” 2012, pp. 55–64.

S. Zida, P. Fournier-Viger, J. C.-W. Lin, C.-W. Wu, and V. S. Tseng, “Efim: a highly efficient algorithm for high-utility itemset mining.” Springer, 2015, pp. 530–546.

L. T. Nguyen, V. V. Vu, M. T. Lam, T. T. Duong, L. T. Manh, T. T. Nguyen, B. Vo, and H. Fujita, “An efficient method for mining high utility closed itemsets,” Information Sciences, vol. 495, pp. 78–99, 8 2019.

S. Krishnamoorthy, “Mining top-k high utility itemsets with effective threshold raising strategies,” Expert Systems with Applications, vol. 117, pp. 148–165, 3 2019.

H. Kim, U. Yun, Y. Baek, J. Kim, B. Vo, E. Yoon, and H. Fujita, “Efficient list based mining of high average utility patterns with maximum average pruning strategies,” Information Sciences, vol. 543, pp. 85–105, 1 2021.

U. Huynh, B. Le, D.-T. Dinh, and H. Fujita, “Multi-core parallel algorithms for hiding high-utility sequential patterns,” Knowledge-Based Systems, vol. 237, p. 107793, 2 2022.

H. M. Huynh, L. T. Nguyen, B. Vo, Z. K. Oplatková, P. Fournier-Viger, and U. Yun, “An efficient parallel algorithm for mining weighted clickstream patterns,” Information Sciences, vol. 582, pp. 349–368, 1 2022.

S. Kumar and K. K. Mohbey, “A review on big data based parallel and distributed approaches of pattern mining,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 5, pp. 1639–1662, 5 2022.

Z. Deng, Z. Wang, and J. Jiang, “A new algorithm for fast mining frequent itemsets using n-lists,” Science China Information Sciences, vol. 55, pp. 2008–2030, 2012.

R. Rymon, “Search through systematic set enumeration,” in Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning, 1992, pp. 539–550.

“Spmf: A java open-source data mining library, retrieved from http://www.philippe-fournierviger.com/spmf/index.php?link=datasets.php (accessed: 20 august 2020).”

Published
2023-10-12