Cover
Vol. 1 No. 1 (2025)

Published: June 30, 2025

Pages: 38-45

Original Article

Mining Dynamic Profit Databases: Efficient Solutions for High Utility Periodic and Closed Patterns

Arkan Ghaib 1 Abdullah Nahi Hussain Younis 2 Takaaki Fujita 3
1Department of Information Technologies Management, Southern Technical University, Basrah, Iraq
2College of Education for Women, University of Basrah, Basrah 61004, Iraq & School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia
3Independent Researcher, Shinjuku, Shinjuku-ku, Tokyo, Japan
History
  • Received: June 15, 2025
  • Revised: June 23, 2025
  • Accepted: June 25, 2025
  • Available Online: June 30, 2025
DOI

https://doi.org/10.33971/ijmrai.1.1.6

Abstract

High utility pattern mining (HUPM) is one of the key areas in data mining, which is concerned with identifying patterns with high utility from transactional databases. The temporal factors such as periodicity and recency along with dynamic variations in profits have recently been added to pattern mining. However, no methods so far unify these dimensions in a common framework. To this end, in this paper we propose the DTU-Miner algorithm that integrates temporal constraints and dynamic profit updates to overcome such limitations. Through the use of advanced data structures such as UPR-List and P-set and the introduction of some novel pruning strategies, DTU-Miner surpasses state of the art in terms of Runtime, Memory and pattern quality. Results on benchmark datasets show that DTU-Miner outperforms state-of-the-art algorithms, CPR-Miner and iEFIM-Closed, which suggests the effectiveness of DTU-Miner over dense and sparse datasets including dynamic attributes.

Keywords

References

  1. J. M. Luna, P. Fournier‐Viger, and S. Ventura, “Frequent itemset mining: A 25 years’ review ,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 9, no. 6, e1329, 2019, https://doi.org/10.1002/widm.1329
  2. B. Abdullah, and Vb. Narasimha, "High Utility Pattern Mining: A Survey on Current and Possible Areas of Applications," Review of Information Engineering and Applications, vol.9, no.1, pp. 38-49, 2022, https://doi.org/10.18488/79.v9i1.3236
  3. P. Fournier-Viger, C. W. Wu, S. Zida, and V. S. Tseng, “FHM: Faster high-utility itemset mining using estimated utility co-occurrence pruning,” in Foundations of Intelligent Systems: Proc. 21st Int. Symp. ISMIS 2014, Roskilde, Denmark, vol. 8502, pp. 83–92, 2014, https://doi.org/10.1007/978-3-319-08326-1_9
  4. Y. Liu, W. K. Liao, and A. Choudhary, “A two-phase algorithm for fast discovery of high utility itemsets,” in Advances in Knowledge Discovery and Data Mining: Proc. 9th Pacific-Asia Conf. PAKDD 2005, Hanoi, Vietnam, vol. 3518, pp. 689–695, 2005, https://doi.org/10.1007/11430919_79
  5. A. A. Al-Hamodi and S. F. Lu, “MapReduce Frequent Itemsets for Mining Association Rules,” in Proc. Int. Conf. Information System and Artificial Intelligence (ISAI), pp. 281–284, IEEE, 2016, https://doi.org/10.1109/ISAI.2016.0066
  6. A. A. Al-Hamodi and S. Lu, “MRFP: discovery frequent patterns using MapReduce frequent pattern growth,” in Proc. Int. Conf. Network and Information Systems for Computers (ICNISC), pp. 298–301, IEEE, 2016, https://doi.org/10.1109/ICNISC.2016.071
  7. Y. Qi, X. Zhang, G. Chen, and W. Gan, “Mining periodic trends via closed high utility patterns,” Expert Systems with Applications, vol. 228, 2023, https://doi.org/10.1016/j.eswa.2023.120356
  8. T. D. Nguyen et al., “Efficient algorithms for mining closed high utility itemsets in dynamic profit databases,” Expert Systems with Applications, vol. 186, 2021, https://doi.org/10.1016/j.eswa.2021.115741
  9. M. Liu and J. Qu, “Mining high utility itemsets without candidate generation,” in Proc. 21st ACM Int. Conf. Information and Knowledge Management, pp. 55–64, 2012, https://doi.org/10.1145/2396761.2396773
  10. Y. Liu, W.-K. Liao, and A. Choudhary, “A two-phase algorithm for fast discovery of high utility itemsets,” in Proc. Pacific-Asia Conf. Knowledge Discovery and Data Mining, pp. 689–695, 2005, https://doi.org/10.1007/11430919_79
  11. P. Fournier-Viger, S. Zida, J. C.-W. Lin, C.-W. Wu, and V. S. Tseng, “EFIM-closed: Fast and memory efficient discovery of closed high-utility itemsets,” in Proc. Int. Conf. Machine Learning and Data Mining in Pattern Recognition, vol. 9729, pp. 199–213, 2016, https://doi.org/10.1007/978-3-319-41920-6_15
  12. V. S. Tseng, C.-W. Wu, B.-E. Shie, and P. S. Yu, “UP-Growth: An efficient algorithm for high utility itemset mining,” in Proc. 16th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, pp. 253–262, 2010, https://doi.org/10.1145/1835804.1835839
  13. S. K. Tanbeer, C. F. Ahmed, B. S. Jeong, and Y. H. Lee, “Discovering periodic frequent patterns in transactional databases,” in Proc. 13th Pacific-Asia Conf. Knowledge Discovery and Data Mining, LNCS, vol. 5476, pp. 242–253, 2009, https://doi.org/10.1007/978-3-642-01307-2_24
  14. K. Amphawan, P. Lenca, and A. Surarerks, “Mining Top-K periodic-frequent pattern from transactional databases without support threshold,” in Proc. 3rd Int. Conf. Advanced Information Technology, CCIS, vol. 55, pp. 18–29, 2009, https://doi.org/10.1007/978-3-642-10392-6_3
  15. D. T. Dinh, B. Le, P. Fournier-Viger, and V. N. Huynh, “An efficient algorithm for mining periodic high-utility sequential patterns,” Applied Intelligence, vol. 48, no. 12, pp. 4694–4714, 2018, https://doi.org/10.1007/s10489-018-1227-x
  16. V. S. Tseng, C.-W. Wu, P. Fournier-Viger, and P. S. Yu, “Efficient algorithms for mining the concise and lossless representation of high utility itemsets,” IEEE Trans. on Knowledge and Data Engineering, vol. 27, no. 3, pp. 726–739, 2014, https://doi.org/10.1109/TKDE.2014.2345377
  17. T.-L. Dam, K. Li, P. Fournier-Viger, and Q.-H. Duong, “CLS-Miner: Efficient and effective closed high-utility itemset mining,” Frontiers of Computer Science, vol. 13, no. 2, pp. 357–381, 2019, https://doi.org/10.1007/s11704-016-6245-4
  18. A. A. Ghaib, Y. E. A. Alsalhi, I. M. Hayder, H. A. Younis, and A. A. Nahi, “Improving the Efficiency of Distributed Utility Item Sets Mining in Relation to Big Data,” Journal of Computer Science and Technology Studies, vol. 5, no. 4, pp. 122–131, 2023, https://doi.org/10.47760/ijcsmc.2024.v13i01.003
  19. A. A. Ghaib and A. A. Nahi, “Enhancing N-List Structure and Performance for Efficient Large Dataset Analysis,” Int. J. of Computer Science and Mobile Computing, vol. 13, no. 1, pp. 49–58, 2024, https://doi.org/10.47760/ijcsmc.2024.v13i01.003
  20. B. A. Shtayt, N. H. Zakaria, and N. H. Harun, “A comprehensive review on medical image steganography based on LSB technique and potential challenges,” Baghdad Science Journal, vol. 18, no. 2 Suppl., pp. 0957–0957, 2021, https://doi.org/10.21123/bsj.2021.18.2(Suppl.).0957
  21. A. A. Ghaib, “DU-HUIM: A novel dynamic utility high-utility itemset miner algorithm for dynamic utility variations in big data,” IRJET, vol. 12, no. 01, pp. 405–410, 2025.
  22. D. Gurakuq. "Analytical analysis of electromagnetic torque and magnet utilization factor for two different PM machines with SPM and HUPM rotor topologies," IEEE Transactions on Magnetics vol. 57, no. 6 pp. 1-9, 2021, https://doi.org/10.1109/TMAG.2021.3069082