Advertisement

Mining High-Average Utility Itemsets with Positive and Negative External Utilities

  • Irfan YildirimEmail author
  • Mete Celik
Article
  • 34 Downloads

Abstract

High-utility itemset mining (HUIM) is an emerging data mining topic. It aims to find the high-utility itemsets by considering both the internal (i.e., quantity) and external (i.e., profit) utilities of items. High-average-utility itemset mining (HAUIM) is an extension of the HUIM, which provides a more fair measurement named average-utility, by taking into account the length of itemsets in addition to their utilities. In the literature, several algorithms have been introduced for mining high-average-utility itemsets (HAUIs). However, these algorithms assume that databases contain only positive utilities. For some real-world applications, on the other hand, databases may also contain negative utilities. In such databases, the proposed algorithms for HAUIM may not discover the complete set of HAUIs since they are designed for only positive utilities. In this study, to discover the correct and complete set of HAUIs with both positive and negative utilities, an algorithm named MHAUIPNU (mining high-average-utility itemsets with positive and negative utilities) is proposed. MHAUIPNU introduces an upper bound model, three pruning strategies, and a data structure. Experimental results show that MHAUIPNU is very efficient in reducing the size of the search space and thus in mining HAUIs with negative utilities.

Keywords

High-average-utility itemset mining Negative utility Utility mining Data mining 

Notes

References

  1. 1.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22(2), 207–216 (1993).  https://doi.org/10.1145/170036.170072 CrossRefGoogle Scholar
  2. 2.
    Chu, C.J., Tseng, V.S., Liang, T.: An efficient algorithm for mining high utility itemsets with negative item values in large databases. Appl. Math. Comput. 215(2), 767–778 (2009).  https://doi.org/10.1016/j.amc.2009.05.066 CrossRefzbMATHGoogle Scholar
  3. 3.
    Deng, Z.H.: DiffNodesets: an efficient structure for fast mining frequent itemsets. Appl. Soft. Comput. 41, 214–223 (2016).  https://doi.org/10.1016/j.asoc.2016.01.010 CrossRefGoogle Scholar
  4. 4.
    Fournier-Viger, P., Gomariz, A., Gueniche, T., Soltani, A., Wu, C.W., Tseng, V.S.: Spmf: a java open-source pattern mining library. J. Mach. Learn. Res. 15, 3389–3393 (2014)zbMATHGoogle Scholar
  5. 5.
    Fournier-Viger, P., Wu, C.W., Zida, S., Tseng, V.S.: FHM: faster high-utility itemset mining using estimated utility co-occurrence pruning. In: Lect. Notes in Comput. Sci., pp. 83–92. Springer International Publishing (2014).  https://doi.org/10.1007/978-3-319-08326-1_9 Google Scholar
  6. 6.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. ACM SIGMOD Rec. 29(2), 1–12 (2000).  https://doi.org/10.1145/335191.335372 CrossRefGoogle Scholar
  7. 7.
    Hong, T.P., Lee, C.H., Wang, S.L.: Effective utility mining with the measure of average utility. Expert Syst. with Appl. 38(7), 8259–8265 (2011).  https://doi.org/10.1016/j.eswa.2011.01.006 CrossRefGoogle Scholar
  8. 8.
    Huang, H., Wu, X., Relue, R.: Mining frequent patterns with the pattern tree. New Gener. Comput. 23(4), 315–337 (2005).  https://doi.org/10.1007/bf03037636 CrossRefzbMATHGoogle Scholar
  9. 9.
    Kim, D., Yun, U.: Efficient algorithm for mining high average-utility itemsets in incremental transaction databases. Appl. Intell. 47(1), 114–131 (2017).  https://doi.org/10.1007/s10489-016-0890-z CrossRefGoogle Scholar
  10. 10.
    Krishnamoorthy, S.: Pruning strategies for mining high utility itemsets. Expert Syst. Appl. 42(5), 2371–2381 (2015).  https://doi.org/10.1016/j.eswa.2014.11.001 CrossRefGoogle Scholar
  11. 11.
    Krishnamoorthy, S.: Efficiently mining high utility itemsets with negative unit profits. Knowl. Based Syst. 145, 1–14 (2018).  https://doi.org/10.1016/j.knosys.2017.12.035 CrossRefGoogle Scholar
  12. 12.
    Lan, G.C., Hong, T.P., Tseng, V.S.: Efficiently mining of high average-utility itemsets with an improved upper-bound strategy. Int. J. Inf. Technol. Decis. Making 11(05), 1009–1030 (2012).  https://doi.org/10.1142/s0219622012500307 CrossRefGoogle Scholar
  13. 13.
    Lan, G.C., Hong, T.P., Tseng, V.S.: A projection-based approach for discovering high average-utility itemsets. J. Inf. Sci. Eng. 28, 193–209 (2012)Google Scholar
  14. 14.
    Lin, C.W., Hong, T.P., Lu, W.H.: Efficiently mining high average utility itemsets with a tree structure. In: Intell. Inf. Database Syst., pp. 131–139. Springer, Berlin (2010).  https://doi.org/10.1007/978-3-642-12145-6_14 CrossRefGoogle Scholar
  15. 15.
    Lin, C.W., Hong, T.P., Lu, W.H.: Using the structure of prelarge trees to incrementally mine frequent itemsets. New Gener. Comput. 28(1), 5–20 (2010).  https://doi.org/10.1007/s00354-008-0072-6 CrossRefzbMATHGoogle Scholar
  16. 16.
    Lin, C.W., Hong, T.P., Lu, W.H.: An effective tree structure for mining high utility itemsets. Expert Syst. Appl. 38(6), 7419–7424 (2011).  https://doi.org/10.1016/j.eswa.2010.12.082 CrossRefGoogle Scholar
  17. 17.
    Lin, J.C.W., Fournier-Viger, P., Gan, W.: FHN: an efficient algorithm for mining high-utility itemsets with negative unit profits. Knowl. Based Syst. 111, 283–298 (2016).  https://doi.org/10.1016/j.knosys.2016.08.022 CrossRefGoogle Scholar
  18. 18.
    Lin, J.C.W., Li, T., Fournier-Viger, P., Hong, T.P., Zhan, J., Voznak, M.: An efficient algorithm to mine high average-utility itemsets. Adv. Eng. Inf. 30(2), 233–243 (2016).  https://doi.org/10.1016/j.aei.2016.04.002 CrossRefGoogle Scholar
  19. 19.
    Lin, J.C.W., Ren, S., Fournier-Viger, P., Hong, T.P.: EHAUPM: efficient high average-utility pattern mining with tighter upper bounds. IEEE Access 5, 12927–12940 (2017).  https://doi.org/10.1109/access.2017.2717438 CrossRefGoogle Scholar
  20. 20.
    Lin, J.C.W., Ren, S., Fournier-Viger, P., Hong, T.P., Su, J.H., Vo, B.: A fast algorithm for mining high average-utility itemsets. Appl. Intell. 47(2), 331–346 (2017).  https://doi.org/10.1007/s10489-017-0896-1 CrossRefGoogle Scholar
  21. 21.
    Lin, J.C.W., Shao, Y., Fournier-Viger, P., Djenouri, Y., Guo, X.: Maintenance algorithm for high average-utility itemsets with transaction deletion. Appl. Intell. 48(10), 3691–3706 (2018).  https://doi.org/10.1007/s10489-018-1180-8 CrossRefGoogle Scholar
  22. 22.
    Liu, J., Wang, K., Fung, B.C.: Mining high utility patterns in one phase without generating candidates. IEEE Trans. Knowl. Data Eng. 28(5), 1245–1257 (2016).  https://doi.org/10.1109/tkde.2015.2510012 CrossRefGoogle Scholar
  23. 23.
    Liu, M., Qu, J.: Mining high utility itemsets without candidate generation. In: Proc. of the 21st ACM Int. Conf. Inf. Knowl. Manag., CIKM (2012).  https://doi.org/10.1145/2396761.2396773
  24. 24.
    Liu, Y., Liao, W.K., Choudhary, A.: A two-phase algorithm for fast discovery of high utility itemsets. In: Adv. Knowl. Discov. Data Min., pp. 689–695. Springer, Berlin (2005).  https://doi.org/10.1007/11430919_79 CrossRefGoogle Scholar
  25. 25.
    Lu, T., Vo, B., Nguyen, H.T., Hong, T.P.: A new method for mining high average utility itemsets. In: Comput. Inf. Syst. Ind. Manag., pp. 33–42. Springer, Berlin (2014).  https://doi.org/10.1007/978-3-662-45237-0_5 CrossRefGoogle Scholar
  26. 26.
    Peng, A.Y., Koh, Y.S., Riddle, P.: mHUIMiner: a fast high utility itemset mining algorithm for sparse datasets. In: Adv. in Knowl. Discov. Data Min., pp. 196–207. Springer International Publishing (2017).  https://doi.org/10.1007/978-3-319-57529-2_16 CrossRefGoogle Scholar
  27. 27.
    Ryang, H., Yun, U.: Indexed list-based high utility pattern mining with utility upper-bound reduction and pattern combination techniques. Knowl. Inf. Syst. 51(2), 627–659 (2016).  https://doi.org/10.1007/s10115-016-0989-x CrossRefGoogle Scholar
  28. 28.
    Singh, K., Shakya, H.K., Singh, A., Biswas, B.: Mining of high-utility itemsets with negative utility. Expert Syst. (2018).  https://doi.org/10.1111/exsy.12296 CrossRefGoogle Scholar
  29. 29.
    Truong, T., Duong, H., Le, H.B., Viger, P.F.: Efficient vertical mining of high average-utility itemsets based on novel upper-bounds. IEEE Trans. Knowl. Data. Eng., pp. 301–314 (2018).  https://doi.org/10.1109/tkde.2018.2833478 CrossRefGoogle Scholar
  30. 30.
    Tseng, V.S., Shie, B.E., Wu, C.W., Yu, P.S.: Efficient algorithms for mining high utility itemsets from transactional databases. IEEE Trans. Knowl. Data Eng. 25(8), 1772–1786 (2013).  https://doi.org/10.1109/tkde.2012.59 CrossRefGoogle Scholar
  31. 31.
    Tseng, V.S., Wu, C.W., Shie, B.E., Yu, P.S.: UP-growth: an efficient algorithm for high utility itemset mining. In: Proc. 16th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. (2010).  https://doi.org/10.1145/1835804.1835839
  32. 32.
    Wu, J.M.T., Lin, J.C.W., Pirouz, M., Fournier-Viger, P.: TUB-HAUPM: tighter upper bound for mining high average-utility patterns. IEEE Access 6, 18655–18669 (2018).  https://doi.org/10.1109/access.2018.2820740 CrossRefGoogle Scholar
  33. 33.
    Wu, T.Y., Lin, J.C.W., Shao, Y., Fournier-Viger, P., Hong, T.P.: Updating the discovered high average-utility patterns with transaction insertion. In: Adv. Intell. Syst. Comput., pp. 66–73. Springer Singapore (2017).  https://doi.org/10.1007/978-981-10-6487-6_9 Google Scholar
  34. 34.
    Yildirim, I., Celik, M.: FIMHAUI: Fast incremental mining of high average-utility itemsets. In: 2018 Int. Conf. on Artif. Intell. and Data Process. (IDAP). IEEE (2018).  https://doi.org/10.1109/idap.2018.8620819
  35. 35.
    Yun, U., Kim, D.: Mining of high average-utility itemsets using novel list structure and pruning strategy. Future Gener. Comput. Syst. 68, 346–360 (2017).  https://doi.org/10.1016/j.future.2016.10.027 CrossRefGoogle Scholar
  36. 36.
    Yun, U., Kim, D., Yoon, E., Fujita, H.: Damped window based high average utility pattern mining over data streams. Knowl. Based Syst. 144, 188–205 (2018).  https://doi.org/10.1016/j.knosys.2017.12.029 CrossRefGoogle Scholar
  37. 37.
    Zida, S., Fournier-Viger, P., Lin, J.C.W., Wu, C.W., Tseng, V.S.: EFIM: a fast and memory efficient algorithm for high-utility itemset mining. Knowl. Inf. Syst. 51(2), 595–625 (2016).  https://doi.org/10.1007/s10115-016-0986-0 CrossRefGoogle Scholar

Copyright information

© Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer EngineeringErciyes UniversityKayseriTurkey

Personalised recommendations