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Positive Correlation Based Efficient High Utility Pattern Mining Approach

Part of the Communications in Computer and Information Science book series (CCIS,volume 1483)

Abstract

The problem of high utility pattern (HUP) mining is an interesting task and comes with various applications. It has been argued in various past studies that the patterns with strong mutual correlation are more useful for decision making. A more powerful tool that takes the inherent correlation into account is more desirable. This paper presents an Efficient Correlated High Utility Miner (ECHUM) that considers the positive correlation among the items to find correlated high utility itemsets. The ECHUM uses a list structure is to avoid multiple scanning of the dataset. The search space is significantly reduced by using two upper-bounds named sub-tree utility and the local utility. Also, several database projection methods and transaction merging methods are used to reduce the complexity. Several pruning strategies are adopted to make the algorithm fast and memory efficient. A variety of experiments conducted shows that ECHUM is 2–3 times faster, and the memory usage is also 3–4 times lesser than the existing algorithm.

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References

  1. Liu, Y., Liao, W., Choudhary, A.: A two-phase algorithm for fast discovery of high utility itemsets. In: Ho, T.B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 689–695. Springer, Heidelberg (2005). https://doi.org/10.1007/11430919_79

    Chapter  Google Scholar 

  2. Ahmed, C.F., Tanbeer, S.K., Jeong, B.S., Lee, Y.K.: Efficient tree structures for high utility pattern mining in incremental databases. IEEE Trans. Knowl. Data Eng. 21(12), 1708–1721 (2009)

    Article  Google Scholar 

  3. Tseng, V.S., Wu, C.W., Shie, B.E., Yu, P.S.: UP-growth: an efficient algorithm for high utility itemset mining. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 253–262 (2010)

    Google Scholar 

  4. Tseng, V.S., Shie, B.E., Wu, C.W., Philip, S.Y.: Efficient algorithms for mining high utility itemsets from transactional databases. IEEE Trans. Knowl. Data Eng. 25(8), 1772–1786 (2012)

    Article  Google Scholar 

  5. Liu, M., Qu, J.: Mining high utility itemsets without candidate generation. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 55–64 (2012)

    Google Scholar 

  6. Fournier-Viger, P., Wu, C..-W.., Zida, S., Tseng, V.S.: FHM: faster high-utility itemset mining using estimated utility co-occurrence pruning. In: Andreasen, T., Christiansen, H., Cubero, J.-C., Raś, Z.W. (eds.) ISMIS 2014. LNCS (LNAI), vol. 8502, pp. 83–92. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08326-1_9

    Chapter  Google Scholar 

  7. Krishnamoorthy, S.: Pruning strategies for mining high utility itemsets. Expert Syst. Appl. 42(5), 2371–2381 (2015)

    Article  Google Scholar 

  8. Zida, S., Fournier-Viger, P., Lin, J.C.-W., Wu, C.-W., Tseng, V.S.: EFIM: a highly efficient algorithm for high-utility itemset mining. In: Sidorov, G., Galicia-Haro, S.N. (eds.) MICAI 2015. LNCS (LNAI), vol. 9413, pp. 530–546. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27060-9_44

    Chapter  Google Scholar 

  9. Gan, W., Lin, J.C.W., Fournier-Viger, P., Chao, H.C., Tseng, V.S., Philip, S.Y.: A survey of utility-oriented pattern mining. IEEE Trans. Knowl. Data Eng. 33(4), 1306–1327 (2019)

    Article  Google Scholar 

  10. Sethi, K.K., Ramesh, D., Sreenu, M.: Parallel high average-utility itemset mining using better search space division approach. In: Fahrnberger, G., Gopinathan, S., Parida, L. (eds.) ICDCIT 2019. LNCS, vol. 11319, pp. 108–124. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05366-6_9

    Chapter  Google Scholar 

  11. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceeding of 20th International Conference on Very Large Databases. VLDB, vol. 1215, pp. 487–499 (1994)

    Google Scholar 

  12. Kim, W.-Y., Lee, Y.-K., Han, J.: CCMine: efficient mining of confidence-closed correlated patterns. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 569–579. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24775-3_68

    Chapter  Google Scholar 

  13. Ahmed, C.F., Tanbeer, S.K., Jeong, B.S., Choi, H.J.: A framework for mining interesting high utility patterns with a strong frequency affinity. Inf. Sci. 181(21), 4878–4894 (2011)

    Google Scholar 

  14. Omiecinski, E.R.: Alternative interest measures for mining associations in databases. IEEE Trans. Knowl. Data Eng. 15(1), 57–69 (2003)

    Article  MathSciNet  Google Scholar 

  15. Lin, J.-W., Gan, W., Fournier-Viger, P., Hong, T.-P., Chao, H.-C.: FDHUP: fast algorithm for mining discriminative high utility patterns. Knowl. Inf. Syst. 51(3), 873–909 (2016). https://doi.org/10.1007/s10115-016-0991-3

    Article  Google Scholar 

  16. Gan, W., Lin, J.C.W., Fournier-Viger, P., Chao, H.C., Fujita, H.: Extracting non-redundant correlated purchase behaviors by utility measure. Knowl.-Based Syst. 143, 30–41 (2018)

    Article  Google Scholar 

  17. Gan, W., Lin, J.C.W., Chao, H.C., Fujita, H., Philip, S.Y.: Correlated utility-based pattern mining. Inf. Sci. 504, 470–486 (2019)

    Article  MathSciNet  Google Scholar 

  18. Kulczynski, S.: Die pflanzenassoziationen der pieninen, Imprimerie del’Universit (1928)

    Google Scholar 

  19. Song, W., Liu, Y., Li, J.: BAHUI: fast and memory efficient mining of high utility itemsets based on bitmap. Int. J. Data Warehousing Min. (IJDWM) 10(1), 1–15 (2014)

    Article  Google Scholar 

  20. 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(1), 3389–3393 (2014)

    Google Scholar 

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Correspondence to Dharavath Ramesh .

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Ramesh, D., Sethi, K.K., Rathore, A. (2021). Positive Correlation Based Efficient High Utility Pattern Mining Approach. In: Venugopal, K.R., Shenoy, P.D., Buyya, R., Patnaik, L.M., Iyengar, S.S. (eds) Data Science and Computational Intelligence. ICInPro 2021. Communications in Computer and Information Science, vol 1483. Springer, Cham. https://doi.org/10.1007/978-3-030-91244-4_22

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  • DOI: https://doi.org/10.1007/978-3-030-91244-4_22

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  • Online ISBN: 978-3-030-91244-4

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