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New Generation Computing

Erschienen in:

07.11.2019

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

verfasst von: Irfan Yildirim, Mete Celik

Erschienen in: New Generation Computing | Ausgabe 1/2020

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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.

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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 CrossRef

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 CrossRefMATH

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 CrossRef

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)MATH

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

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 CrossRef

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 CrossRef

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 CrossRefMATH

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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)

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

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 CrossRefMATH

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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.

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

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

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

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 CrossRef

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 CrossRef

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

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 CrossRef

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.

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 CrossRef

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

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.

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 CrossRef

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 CrossRef

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 CrossRef

Titel: Mining High-Average Utility Itemsets with Positive and Negative External Utilities
verfasst von: Irfan Yildirim
Mete Celik
Publikationsdatum: 07.11.2019
Verlag: Ohmsha
Erschienen in: New Generation Computing / Ausgabe 1/2020
Print ISSN: 0288-3635
Elektronische ISSN: 1882-7055
DOI: https://doi.org/10.1007/s00354-019-00078-8

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