An Efficient Pruned Matrix Aided Utility Tree for High Utility Itemset Mining from Transactional Database
Keywords:
Internal utility, unit profit, external utility, pruned matrix, frequent pattern, transaction weighted utility model, threshold value, and treeAbstract
High Utility Itemset Mining (HUIM) is the progression of identifying highly profitable items by considering the unit profit of the item from the huge transactional database. HUIM is an essential subject with broad applications in recent years. HUIM paves the way to know the profitable items using factors namely profit and quantity. Until today, abundant algorithms have been found to mine High Utility Itemset (HUI) and it is entirely different from the conventional mining algorithms. Most of the utility mining algorithms generate the itemset recurrently and scan the database redundantly, which leads to computational complexity. To overcome this issue, a pruning technique is introduced with a matrix and Frequent Pattern (FP) tree is constructed with the pruned matrix whereby the complexity in HUI identification is minimized. Experimental results are investigated using a benchmark dataset and the outcome depicts that the proposed pruned matrix-aided utility tree (PMAUT) outperforms the existing state of art techniques in terms of time consumption and memory usage.
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Fournier-Viger, P., Chun-Wei Lin, J., Truong-Chi, T., & Nkambou, R. (2019). A survey of high utility itemset mining. High-utility pattern mining: Theory, algorithms and applications, 1-45.
Gan, W., Lin, J. C. W., Fournier-Viger, P., Chao, H. C., Tseng, V. S., & Philip, S. Y. (2019). A survey of utility-oriented pattern mining. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1306-1327.
Tharini, V. J., & Shivakumar, B. L. High-Utility Itemset Mining: Fundamentals, Properties, Techniques and Research Scope. In Computational Intelligence and Data Sciences (pp. 195-210). CRC Press.
Yen, S. J., & Lee, Y. S. (2007, September). Mining high utility quantitative association rules. In International Conference on Data Warehousing and Knowledge Discovery (pp. 283-292). Springer, Berlin, Heidelberg.
R. Chan, Q. Yang, and Y. Shen, ―Mining high utility Itemsets,‖ The Third IEEE International Conference on Data Mining, pp. 19-26, 2003.
Liu, Y., Liao, W. K., &Choudhary, A. (2005, May). A two-phase algorithm for fast discovery of high utility itemsets. In Pacific-Asia Conference on Knowledge Discovery and Data Mining(pp. 689-695). Springer, Berlin, Heidelberg.
J. Han, J. Pei, Y. Yin, ―Mining frequent patterns without candidate generation‖, in Proceedings of the ACM-SIGMOD Int'l Conf. on Management of Data, pp. 1-12.
Lan, G. C., Hong, T. P., & Tseng, V. S. (2014). An efficient projection-based indexing approach for mining high utility itemsets. Knowledge and information systems, 38(1), 85-107.
Jeevika Tharini, V., & Vijayarani, S. (2020). Bio-inspired High-Utility Item Framework based Particle Swarm Optimization Tree Algorithms for Mining High Utility Itemset. In Advances in Computational Intelligence and Informatics: Proceedings of ICACII 2019 (pp. 265-276). Springer Singapore.
Chan R, Yang Q, Shen Y-D (2003) Mining high utility itemsets. In: Proceedings of 3rd IEEE international conference data mining, 2003, (Washington, D.C., USA, 2003) pp. 19–22
Yao H, Hamilton HJ, Butz CJ (2004) A foundational approach to mining itemset utilities from databases. In: Proceedings of 3rd SIAM international conference on data mining, 2004, (Orlando, Florida, USA, 2004) pp 482–486
Gan W, Lin JC-W, Fournier-Viger P, Chao H-C, Tseng VS, Yu PS (2018) A survey of utility-oriented pattern mining. arXiv: 1805.10511
Rahmati B, Sohrabi MK (2019) A systematic survey of high utility itemset mining. Int J Inf Technol Decis Mak 18(4):1113–1185
Krishnamoorthy S (2019) Mining top-k high utility itemsets with effective threshold raising strategies. Expert Syst Appl 117:148–165
Sharmila, P., & Meenakshi, S. (2018). AN ENHNACED HIGH UTILITY PATTERN APPROACH FOR MINING ITEMSETS. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 7(1).
Fournier-Viger, P., Zhang, Y., Lin, J. C. W., Dinh, D. T., & Bac Le, H. (2020). Mining correlated high-utility itemsets using various measures. Logic Journal of the IGPL, 28(1), 19-32.
Kannimuthu S, Premalatha K (2014) Discovery of high utility itemsets using genetic algorithm with ranked mutation. Appl Artif Intel 28(4):337–359
Wu JM-T, Zhan J, Lin JC-W (2017) An ACO-based approach to mine high-utility itemsets. Knowl-Based Syst 116:102–113
Tamilselvi T, Arasun GT (2019) Handling high web access utility mining using intelligent hybrid hill climbing algorithm based tree construction. Clust Comput 22:145–155
Bakariya B, Thakur GS (2015) An efficient algorithm for extracting high utility itemsets from weblog data. IETE Tech Rev 32(2):151–160
Choi H-J, Park CH (2019) Emerging topic detection in twitter stream based on high utility pattern mining. Exp Syst Appl 115:27–36
Gan W, Lin JC-W, Fournier-Viger P, Chao H-C, Fujita H (2018) Extracting non-redundant correlated purchase behaviors by utility measure. Knowl-Based Syst 143:30–41
Weng C-H (2016) Discovering highly expected utility itemsets for revenue prediction. Knowl-Based Syst 104:39–51
Yun U, Lee G, Yoon E (2017) Efficient high utility pattern mining for establishing manufacturing plans with sliding window control. IEEE Trans Ind Electron 64(9):7239–7249
Kannimuthu S, Premalatha K, Shankar S (2012) Investigation of high utility itemset mining in service oriented computing: deployment of knowledge as a service in E-commerce. In: 2012 fourth international conference on advanced computing (ICoAC), pp 1–8
Yang R, Xu M, Jones P, Samatova N (2017) Real time utility-based recommendation for revenue optimization via an adaptive online Top-K high utility itemsets mining model. In: 13th international conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD), pp 1859–1866
Shie B-E, Yu PS, Tseng VS (2013) Mining interesting user behavior patterns in mobile commerce environments. Appl Intell 38(3):418–435
Fournier-Viger P, Gomariz A, Soltani A, Lam H, Gueniche T (2014) SPMF: open-source data mining platform. http://www.philippe-fournier-viger.com/spmf
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