A Fast and Enhanced Approach for High Average Utility Itemset Mining with Lossy Items
Keywords:
High average-utility itemsets Negative utility, Premature pruning strategies, Tighter upper bound, Appropriate maximal utilityAbstract
Existing utility mining algorithms consider only frequency, productivity and quantity of every item purchased for utility calculation. No attention has been given to the length of transactions. Uncovering high average utility itemsets from the transaction dataset solves the above issue. In some circumstances, things may appear in reality with a negative unit value. For example, a retail store may trade items at a loss to energize the trade of similar products. To resolve the issue, we propose an effective mechanism named appropriate average high utility itemset mining with negative utilities (AAHUIM-NU) which creates high quality decision makers. To solve the issue of data set repository and numerous sweeps of the data set, proposed algorithm involves number of lists for caching the relevant data which possesses only less storage. It utilizes a minimized transaction utility, decreased maximal utility, and generalized tight upper bounds to find out the pruning threshold and to minimize the running time and memory. Exploratory outcomes on datasets demonstrate that AAHUIM-NU is productive in terms of processing time, memory usage, and versatility. It performs well on thick datasets which has an excessive number of long transactions. The experimental results are recorded in tables and given in this paper.
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References
R. Agrawal and R. S&ant, “Fast Algorithms for Mining Association Rules .,” In Proc. Int’l Conf.Very Large Data Bases, pp. 487–499, 1994.
P. Naresh and R. Suguna, “IPOC: An efficient approach for dynamic association rule generation using incremental data with updating supports,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 24, no. 2, pp. 1084–1090, Nov. 2021, doi: 10.11591/ijeecs.v24.i2.pp1084-1090.
A. G. Shaaban, M. H. Khafagy, M. A. Elmasry, H. El-Beih, and M. H. Ibrahim, “Knowledge discovery in manufacturing datasets using data mining techniques to improve business performance,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 26, no. 3, pp. 1736–1746, Jun. 2022, doi: 10.11591/ijeecs.v26.i3.pp1736-1746.
T. B. Ho, D. Cheung, H. Liu, Y. Liu, W.-K. Liao, and A. Choudhary, “A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets,” 2005.
C. F. Ahmed, S. K. Tanbeer, B. S. Jeong, and Y. K. Lee, “Efficient tree structures for high utility pattern mining in incremental databases,” IEEE Trans Knowl Data Eng, vol. 21, no. 12, pp. 1708–1721, Dec. 2009, doi: 10.1109/TKDE.2009.46.
B. Vo, H. Nguyen, T. B. Ho, and B. Le, “Parallel method for mining high utility itemsets from vertically partitioned distributed databases,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, pp. 251–260. doi: 10.1007/978-3-642-04595-0_31.
Y. C. Li, J. S. Yeh, and C. C. Chang, “Isolated items discarding strategy for discovering high utility itemsets,” Data Knowl Eng, vol. 64, no. 1, pp. 198–217, Jan. 2008, doi: 10.1016/j.datak.2007.06.009.
V. S. Tseng, C. W. Wu, B. E. Shie, and P. S. Yu, “UP-Growth: An efficient algorithm for high utility itemset mining,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010, pp. 253–262. doi: 10.1145/1835804.1835839.
V. S. Tseng, B. E. Shie, C. W. Wu, and P. S. Yu, “Efficient algorithms for mining high utility itemsets from transactional databases,” IEEE Trans Knowl Data Eng, vol. 25, no. 8, pp. 1772–1786, Aug. 2013, doi: 10.1109/TKDE.2012.59.
Mengchi Liu and Junfeng Qu, “Mining High Utility Itemsets without Candidate Generation,” CIKM’12, ACM, pp. 55–64, 2012.
T. P. Hong, C. H. Lee, and S. L. Wang, “Effective utility mining with the measure of average utility,” Expert Syst Appl, vol. 38, no. 7, pp. 8259–8265, Jul. 2011, doi: 10.1016/j.eswa.2011.01.006.
G. C. Lan, T. P. Hong, and V. S. Tseng, “An efficient projection-based indexing approach for mining high utility itemsets,” Knowl Inf Syst, vol. 38, no. 1, pp. 85–107, 2014, doi: 10.1007/s10115-012-0492-y.
Rajendra, K. ., Subramanian, S. ., Karthik, N. ., Naveenkumar, K. ., & Ganesan, S. . (2023). Grey Wolf Optimizer and Cuckoo Search Algorithm for Electric Power System State Estimation with Load Uncertainty and False Data. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 59–67. https://doi.org/10.17762/ijritcc.v11i2s.6029
G. C. Lan, T. P. Hong, and V. S. Tseng, “Efficiently mining high average-utility itemsets with an improved upper-bound strategy,” Int J Inf Technol Decis Mak, vol. 11, no. 5, pp. 1009–1030, Sep. 2012, doi: 10.1142/S0219622012500307.
C.-W. Lin, T.-P. Hong, and W.-H. Lu, “Efficiently Mining High Average Utility Itemsets with a Tree Structure,” Proc. IIDS, Springer, pp. 131–139, 2010.
T. Lu, B. Vo, H. T. Nguyen, and T.-P. Hong, “A New Method for Mining High Average Utility Itemsets,” in LNCS, 2014, pp. 33–42.
J. C. W. Lin, T. Li, P. Fournier-Viger, T. P. Hong, J. Zhan, and M. Voznak, “An efficient algorithm to mine high average-utility itemsets,” Advanced Engineering Informatics, vol. 30, no. 2, pp. 233–243, Apr. 2016, doi: 10.1016/j.aei.2016.04.002.
J. C. W. Lin, S. Ren, P. Fournier-Viger, and T. P. Hong, “EHAUPM: Efficient High Average-Utility Pattern Mining with Tighter Upper Bounds,” IEEE Access, vol. 5, pp. 12927–12940, Jun. 2017, doi: 10.1109/ACCESS.2017.2717438.
U. Yun and D. Kim, “Mining of high average-utility itemsets using novel list structure and pruning strategy,” Future Generation Computer Systems, vol. 68, pp. 346–360, Mar. 2017, doi: 10.1016/j.future.2016.10.027.
J. C. W. Lin, S. Ren, P. Fournier-Viger, T. P. Hong, J. H. Su, and B. Vo, “A fast algorithm for mining high average-utility itemsets,” Applied Intelligence, vol. 47, no. 2, pp. 331–346, Sep. 2017, doi: 10.1007/s10489-017-0896-1.
Prof. Amruta Bijwar. (2016). Design and Analysis of High Speed Low Power Hybrid Adder Using Transmission Gates. International Journal of New Practices in Management and Engineering, 5(03), 07 - 12. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/46
J. M. T. Wu, J. C. W. Lin, M. Pirouz, and P. Fournier-Viger, “TUB-HAUPM: Tighter Upper Bound for Mining High Average-Utility Patterns,” IEEE Access, vol. 6, pp. 18655–18669, 2018, doi: 10.1109/ACCESS.2018.2820740.
T. Truong, H. Duong, B. Le, and P. Fournier-Viger, “Efficient Vertical Mining of High Average-Utility Itemsets Based on Novel Upper-Bounds,” IEEE Trans Knowl Data Eng, vol. 31, no. 2, pp. 301–314, Feb. 2019, doi: 10.1109/TKDE.2018.2833478.
C. J. Chu, V. S. Tseng, and T. Liang, “An efficient algorithm for mining high utility itemsets with negative item values in large databases,” Appl Math Comput, vol. 215, no. 2, pp. 767–778, Sep. 2009, doi: 10.1016/j.amc.2009.05.066.
S. Krishnamoorthy, “Efficiently mining high utility itemsets with negative unit profits,” Knowl Based Syst, vol. 145, pp. 1–14, Apr. 2018, doi: 10.1016/j.knosys.2017.12.035.
J. C. W. Lin, P. Fournier-Viger, and W. Gan, “FHN: An efficient algorithm for mining high-utility itemsets with negative unit profits,” Knowl Based Syst, vol. 111, pp. 283–298, Nov. 2016, doi: 10.1016/j.knosys.2016.08.022.
K. Singh, H. K. Shakya, A. Singh, and B. Biswas, “Mining of high-utility itemsets with negative utility,” Expert Syst, vol. 35, no. 6, Dec. 2018, doi: 10.1111/exsy.12296.
I. Yildirim and M. Celik, “Mining High-Average Utility Itemsets with Positive and Negative External Utilities,” New Gener Comput, vol. 38, no. 1, pp. 153–186, Mar. 2020, doi: 10.1007/s00354-019-00078-8.
G. C. Lan, T. P. Hong, J. P. Huang, and V. S. Tseng, “On-shelf utility mining with negative item values,” Expert Syst Appl, vol. 41, no. 7, pp. 3450–3459, Jun. 2014, doi: 10.1016/j.eswa.2013.10.049.
K. Singh, A. Kumar, S. S. Singh, H. K. Shakya, and B. Biswas, “EHNL: An efficient algorithm for mining high utility itemsets with negative utility value and length constraints,” Inf Sci (N Y), vol. 484, pp. 44–70, May 2019, doi: 10.1016/j.ins.2019.01.056.
Prof. Madhuri Zambre. (2016). Automatic Vehicle Over speed Controlling System using Microcontroller Unit and ARCAD. International Journal of New Practices in Management and Engineering, 5(04), 01 - 05. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/47
Souleymane Zida, Philippe Fournier-Viger, Jerry Chun-Wei Lin, Cheng-Wei Wu, and Vincent S. Tseng, “EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining,” LNAI, vol. 9413, pp. 530–546, 2015, doi: 10.1007/978-3-319-27060-9.
W. Gan, J. C. W. Lin, P. Fournier-Viger, H. C. Chao, and V. S. Tseng, “Mining high-utility itemsets with both positive and negative unit profits from uncertain databases,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 2017, pp. 434–446. doi: 10.1007/978-3-319-57454-7_34.
K. Singh, S. S. Singh, A. Kumar, H. K. Shakya, and B. Biswas, “CHN: an efficient algorithm for mining closed high utility itemsets with negative utility,” IEEE Trans Knowl Data Eng, 2018, doi: 10.1109/TKDE.2018.2882421.
P. Fournier-Viger, A. Gomariz, A. Soltani, C.-W. Wu, and V. S. Tseng, “SPMF: A Java Open-Source Pattern Mining Library,” 2014.
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