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Mining Non-redundant Periodic Frequent Patterns

  • Michael Kofi Afriyie
  • Vincent Mwintieru NofongEmail author
  • John Wondoh
  • Hamidu Abdel-Fatao
Conference paper
  • 303 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12033)

Abstract

Discovering periodic frequent patterns has been useful in various decision making. Traditional algorithms, however, often report a large number of such patterns, most of which are often redundant since their periodic occurrences can be inferred from other periodic frequent patterns. Employing such redundant periodic frequent patterns in decision making would often be detrimental if not trivial. To address this challenge and report only non-redundant periodic frequent patterns, this paper employs the concept of deduction rules in mining the set of non-redundant periodic frequent patterns. A Non-redundant Periodic Frequent Pattern Miner (NPFPM) is subsequently proposed to achieve this purpose. Experimental analysis on benchmark datasets show that NPFPM is efficient and can effectively prune the set of redundant periodic frequent patterns.

Keywords

Frequent patterns Periodic frequent patterns Non-redundance 

References

  1. 1.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207–216 (1993). ACMCrossRefGoogle Scholar
  2. 2.
    Fournier-Viger, P., et al.: PFPM: discovering periodic frequent patterns with novel periodicity measures. In: Proceedings of the 2nd Czech-China Scientific Conference 2017. InTechGoogle Scholar
  3. 3.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. ACM SIGMOD Rec. 29(2), 1–12 (2000). ACMCrossRefGoogle Scholar
  4. 4.
    Ismail, W.N., Hassan, M.M., Alsalamah, H.A., Fortino, G.: Mining productive-periodic frequent patterns in tele-health systems. J. Netw. Comput. Appl. 115, 33–47 (2018)CrossRefGoogle Scholar
  5. 5.
    Uday Kiran, R., Krishna Reddy, P.: Towards efficient mining of periodic-frequent patterns in transactional databases. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds.) DEXA 2010. LNCS, vol. 6262, pp. 194–208. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15251-1_16CrossRefGoogle Scholar
  6. 6.
    Kiran, R.U., Kitsuregawa, M.: Novel techniques to reduce search space in periodic-frequent pattern mining. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds.) DASFAA 2014. LNCS, vol. 8422, pp. 377–391. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-05813-9_25CrossRefGoogle Scholar
  7. 7.
    Kiran, R.U., Kitsuregawa, M.: Discovering Quasi-periodic-frequent patterns in transactional databases. In: Bhatnagar, V., Srinivasa, S. (eds.) BDA 2013. LNCS, vol. 8302, pp. 97–115. Springer, Cham (2013).  https://doi.org/10.1007/978-3-319-03689-2_7CrossRefGoogle Scholar
  8. 8.
    Kiran, R.U., Reddy, P.K.: An alternative interestingness measure for mining periodic-frequent patterns. In: Yu, J.X., Kim, M.H., Unland, R. (eds.) DASFAA 2011. LNCS, vol. 6587, pp. 183–192. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-20149-3_15CrossRefGoogle Scholar
  9. 9.
    Kumar, V., Valli Kumari, V.: Incremental mining for regular frequent patterns in vertical format. Int. J. Eng. Tech. 5(2), 1506–1511 (2013)Google Scholar
  10. 10.
    Li, J., Li, H., Wong, L., Pei, J., Dong, G.: Minimum description length principle: generators are preferable to closed patterns. In: Proceedings of the 21st National Conference on Artificial Intelligence, pp. 409–414 (2006)Google Scholar
  11. 11.
    Lin, J.C.W., Zhang, J., Fournier-Viger, P., Hong, T.P., Zhang, J.: A two-phase approach to mine short-period high-utility itemsets in transactional databases. Adv. Eng. Inf. 33, 29–43 (2017)CrossRefGoogle Scholar
  12. 12.
    Nofong, V.M.: Discovering productive periodic frequent patterns in transactional databases. Ann. Data Sci. 3(3), 235–249 (2016)CrossRefGoogle Scholar
  13. 13.
    Nofong, V.M.: Fast and memory efficient mining of periodic frequent patterns. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q.T. (eds.) Modern Approaches for Intelligent Information and Database Systems, SCI, vol. 769, pp. 223–232. Springer, Cham (2018)CrossRefGoogle Scholar
  14. 14.
    Nofong, V.M., Wondoh, J.: Towards fast and memory efficient discovery of periodic frequent patterns. J. Inf. Telecommun. 3(4), 480–493 (2019) Google Scholar
  15. 15.
    Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., Yang, D.: H-mine: hyper-structure mining of frequent patterns in large databases. In: Proceedings IEEE International Conference on Data Mining, pp. 441–448, IEEE (2001)Google Scholar
  16. 16.
    Rashid, M.M., Karim, M.R., Jeong, B.-S., Choi, H.-J.: Efficient mining regularly frequent patterns in transactional databases. In: Lee, S., Peng, Z., Zhou, X., Moon, Y.-S., Unland, R., Yoo, J. (eds.) DASFAA 2012. LNCS, vol. 7238, pp. 258–271. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-29038-1_20CrossRefGoogle Scholar
  17. 17.
    Rashid, M.M., Gondal, I., Kamruzzaman, J.: Regularly frequent patterns mining from sensor data stream. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013. LNCS, vol. 8227, pp. 417–424. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-42042-9_52CrossRefGoogle Scholar
  18. 18.
    Surana, A., Kiran, R.U., Reddy, P.K.: An efficient approach to mine periodic-frequent patterns in transactional databases. In: Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J. (eds.) PAKDD 2011. LNCS (LNAI), vol. 7104, pp. 254–266. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-28320-8_22CrossRefGoogle Scholar
  19. 19.
    Tanbeer, S.K., Ahmed, C.F., Jeong, B.-S., Lee, Y.-K.: Discovering periodic-frequent patterns in transactional databases. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS (LNAI), vol. 5476, pp. 242–253. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-01307-2_24CrossRefGoogle Scholar
  20. 20.
    Tseng, F.C.: Mining frequent itemsets in large databases: the hierarchical partitioning approach. Expert Syst. Appl. 40(5), 1654–1661 (2013)CrossRefGoogle Scholar
  21. 21.
    Webb, G.I.: Self-sufficient itemsets: an approach to screening potentially interesting associations between Items. ACM Trans. Knowl. Discov. Data 4(1), 3:1–3:20 (2010)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)CrossRefGoogle Scholar
  23. 23.
    Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W.: Parallel algorithms for discovery of association rules. Data Min. Knowl. Disc. 1(4), 343–373 (1997)CrossRefGoogle Scholar
  24. 24.
    Zaki, M.J., Gouda, K.: Fast vertical mining using diffsets. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 326–335 (2003)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Michael Kofi Afriyie
    • 1
  • Vincent Mwintieru Nofong
    • 1
    Email author
  • John Wondoh
    • 2
  • Hamidu Abdel-Fatao
    • 1
  1. 1.University of Mines and TechnologyTarkwaGhana
  2. 2.University of South of AustraliaAdelaideAustralia

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