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Discovering Self-reliant Periodic Frequent Patterns

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Periodic Pattern Mining

Abstract

Periodic frequent pattern discovery is a non-trivial task for analysing databases to reveal the recurring shapes of patterns’ occurrences. Though significant strides have been made in their discovery for understanding large databases in decision-making, existing techniques still face a challenge of reporting a large number of periodic frequent patterns, most of which are often not useful as their periodic occurrences are either by random chance or can be inferred from the periodicities of other periodic frequent patterns. Reporting such periodic frequent patterns not only degrades the performance of existing algorithms but also could adversely affect decision-making. This study addresses these issues by proposing a novel algorithm named SRPFPM (Self-Reliant Periodic Frequent Pattern Miner) for mining and reporting the set of self-reliant periodic frequent patterns as those whose periodic occurrences have inherent item relationships and cannot be inferred from other periodic frequent patterns. Experimental analysis on benchmark datasets show that SRPFPM is efficient and effectively prunes periodic frequent patterns that are periodic due to random chance as well as those whose periodicities can be inferred from other periodic frequent patterns.

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Notes

  1. 1.

    These implementations are based on novel periodicity measures in Definition 7 and not the measures in periodicity measures employed in the original implementations found in [1, 2, 24]

References

  1. M.K. Afriyie, V.M. Nofong, J. Wondoh and H. Abdel-Fatao, Mining Non-redundant Periodic Frequent Patterns, in Intelligent Information and Database Systems. LNCS, ed. by N.T. Nguyen, K. Jearanaitanakij, A. Selamat, B. Trawiński and S. Chittayasothorn, vol. 12033 (Springer, Berlin, 2020), pp. 321-331

    Google Scholar 

  2. Afriyie, M. K, Nofong, V. M, Wondoh, J. & Abdel-Fatao, H.:Efficient Mining of Non-redundant Periodic Frequent Patterns. Vietnam Journal of Computer Science, 8(4) 1-15 (2021)

    Google Scholar 

  3. Agrawal, R., Imieliński, T. & Swami, A.: Mining Association Rules between Sets of Items in Large Databases. SIGMOD Rec. 22(2) 207–216. ACM (1993)

    Google Scholar 

  4. Amphawan K, Surarerks A, Lenca P: Mining Periodic-Frequent Itemsets with Approximate Periodicity using Interval Transaction-ids List Tree. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining. pp 245-248 (2010)

    Google Scholar 

  5. Fournier-Viger, P., Lin, J. C. W., Gomariz, A., Gueniche, T., Soltani, A., Deng, Z., & Lam, H. T.: The SPMF Open-Source Data Mining Library Version 2. In: Proceedings of European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 36-40, Springer, Cham (2016)

    Google Scholar 

  6. Fournier-Viger, P., Lin, CW., Duong, QH., Dam, TL., Ševčík, L., Uhrin, D., & Voznak, M.: PFPM: Discovering Periodic Frequent Patterns with Novel Periodicity Measures. In: Proceedings of the 2nd Czech-China Scientific Conference, InTech (2017)

    Google Scholar 

  7. Fournier-Viger P., Li Z., Lin, J.C.W., Kiran R.U., Fujita H.: Discovering Periodic Patterns Common to Multiple Sequences. In: Ordonez C., Bellatreche L. (eds) Big Data Analytics and Knowledge Discovery. LNCS, vol 11031, pp 231-246 (2018)

    Google Scholar 

  8. Fournier-Viger P., Yang P., Lin J.C.W., Kiran R.U. (2019) Discovering Stable Periodic-Frequent Patterns in Transactional Data. In: Wotawa F., Friedrich G., Pill I., Koitz-Hristov R., Ali M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. LNCS, vol 11606, pp 230-244 (2019)

    Google Scholar 

  9. Han, J., Pei, J., & Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: ACM SIGMOD Rec. 29(2) 1-12. ACM (2000)

    Google Scholar 

  10. W.N. Ismail, M.M. Hassan, H.A. Alsalamah, Mining of Productive Periodic-Frequent Patterns for IoT Data Analytics. Future Generation Computer Systems 88, 512–523 (2018)

    Article  Google Scholar 

  11. W.N. Ismail, M.M. Hassan, H.A. Alsalamah, G. Fortino, Mining Productive-Periodic Frequent Patterns in Tele-Health Systems. Journal of Network and Computer Applications 115, 33–47 (2018)

    Article  Google Scholar 

  12. 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 International Publishing (2014)

    Google Scholar 

  13. 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 International Publishing (2013)

    Google Scholar 

  14. R.U. Kiran, P.K. Reddy, Towards Efficient Mining of Periodic-Frequent Patterns in Transactional Databases, in DASFAA 2010, ed. by P.G. Bringas, A. Hameurlain, G. Quirchmayr. LNCS, vol. 6262 (Springer, Heidelberg, 2010), pp. 194–208

    Google Scholar 

  15. R.U. Kiran, P.K. Reddy, An Alternative Interestingness Measure for Mining Periodic-Frequent Patterns, in DASFAA 2011, LNCS, vol. 6587, ed. by J.X. Yu, M.H. Kim, R. Unland (Springer, Heidelberg, 2011), pp. 183–192

    Google Scholar 

  16. R.U. Kiran, M. Kitsuregawa, P.K. Reddy, Efficient Discovery of Periodic-Frequent Patterns in Very Large Databases. Journal of Systems and Software 112, 110–121 (2016)

    Article  Google Scholar 

  17. Kiran R.U., Venkatesh J.N., Fournier-Viger P., Toyoda M., Reddy P.K., Kitsuregawa M.: Discovering Periodic Patterns in Non-uniform Temporal Databases. In: Kim J., Shim K., Cao L., Lee JG., Lin X., Moon YS. (eds) Advances in Knowledge Discovery and Data Mining. LNCS, vol 10235, pp 604-617 (2017.)

    Google Scholar 

  18. R.U. Kiran, A. Anirudh, C. Saideep, M. Toyoda, P.K. Reddy, M. Kitsuregawa, Finding Periodic-Frequent Patterns in Temporal Databases using Periodic Summaries. Data Science and Pattern Recognition 3(2), 24–46 (2019)

    Google Scholar 

  19. Kiran, R.U., Saideep, C., Zettsu, K., Toyoda, M., Kitsuregawa, M., Reddy, P.K.: Discovering Partial Periodic Spatial Patterns in Spatiotemporal Databases. In: Proceedings of the 2019 IEEE International Conference on Big Data. IEEE, pp 233-238 (2019)

    Google Scholar 

  20. Kiran R.U. & Reddy P.K.: Mining Rare Periodic-Frequent Patterns using Multiple Minimum Supports. In: Proceedings of the 15th International Conference on Management of Data. pp 7-8 (2010)

    Google Scholar 

  21. V. Kumar, V. Valli-Kumari, Incremental Mining for Regular Frequent Patterns in Vertical Format. Int. J. Eng & Tech. 5(2), 1506–1511 (2013)

    Google Scholar 

  22. 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 

  23. J.C.W. Lin, J. Zhang, P. Fournier-Viger, T.P. Hong, J. Zhang, A Two-Phase Approach to Mine Short-Period High-Utility Itemsets in Transactional Databases. Adv. Eng. Inf. 33, 29–43 (2017)

    Article  Google Scholar 

  24. V.M. Nofong, Discovering Productive Periodic Frequent Patterns in Transactional Databases. Annals of Data Science 3(3), 235–249 (2016)

    Article  Google Scholar 

  25. V.M. Nofong, J. Wondoh, Towards Fast and Memory Efficient Discovery of Periodic Frequent Patterns. Journal of Information and Telecommunication 3(4), 480–493 (2019)

    Article  Google Scholar 

  26. Nofong, V. M. (2018). 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)

    Google Scholar 

  27. 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 

  28. M.M. Rashid, M.R. Karim, B.S. Jeong, H.J. Choi, Efficient Mining Regularly Frequent Patterns in Transactional Databases, in DASFAA 2012, LNCS, vol. 7238, ed. by S. Lee, Z. Peng, X. Zhou, Y. Moon, R. Unland, J. Yoo (Springer, Heidelberg, 2012), pp. 258–271

    Google Scholar 

  29. M.M. Rashid, I. Gondal, J. Kamruzzaman, Regularly Frequent Patterns Mining from Sensor Data Stream, in NIP 2013, LNCS, vol. 8227, ed. by M. Lee, A. Hirose, Z.G. Hou, R. Kil (Springer, Berlin Heidelberg, 2013), pp. 417–424

    Google Scholar 

  30. Shenoy, P., Haritsa, J. R., Sudarshan, S., Bhalotia, G., Bawa, M., & Shah, D.: Turbo-charging Vertical Mining of Large Databases. In ACM SIGMOD Record 29(2), 22-33. ACM (2000)

    Google Scholar 

  31. A. Surana, R.U. Kiran, P.K. Reddy, An Efficient Approach to Mine Periodic-Frequent Patterns in Transactional Databases, in LNAI, vol. 7104, ed. by L. Cao, J.Z. Huang, J. Bailey, Y.S. Koh, J. Luo (Springer, Heidelberg, 2012), pp. 254–266

    Google Scholar 

  32. S.K. Tanbeer, C.F. Ahmed, B.S. Jeong, Y.K. Lee, Discovering Periodic-Frequent Patterns in Transactional Databases, in PAKDD 2009, LNAI, vol. 5476, ed. by T. Theeramunkong, B. Kijsirikul, N. Cercone, T. Ho (Springer, Heidelberg, 2009), pp. 242–253

    Google Scholar 

  33. F.C. Tseng, Mining Frequent Itemsets in Large Databases: The Hierarchical Partitioning Approach. Expert Systems with Applications 40(5), 1654–1661 (2013)

    Article  Google Scholar 

  34. Venkatesh J.N., Uday Kiran R., Krishna Reddy P., & Kitsuregawa M.: Discovering Periodic-Correlated Patterns in Temporal Databases. In: Hameurlain A., Wagner R., Hartmann S., Ma H. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVIII. LNCS, vol 11250, pp. 146-172 (2018)

    Google Scholar 

  35. Zaki, M. J. (2000). Scalable Algorithms for Association Mining. IEEE Transactions on Knowledge and Data Engineering, 12(3), 372-390 (2000)

    Google Scholar 

  36. M.J. Zaki, S. Parthasarathy, M. Ogihara, W. Li, Parallel Algorithms for Discovery of Association Rules. Data Mining and Knowledge Discovery 1(4), 343–373 (1997)

    Article  Google Scholar 

  37. 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 

  38. D. Zhang, K. Lee, I. Lee, Mining hierarchical semantic periodic patterns from GPS-collected spatio-temporal trajectories. Expert Systems with Applications. 122, 85–101 (2019)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Mines and Technology, P. O. Box 273, Tarkwa, Ghana

    Vincent Mwintieru Nofong, Hamidu Abdel-Fatao & Michael Kofi Afriyie

  2. University of South Australia, Mawsonlakes Campus, Adelaide, SA, Australia

    John Wondoh

Authors
  1. Vincent Mwintieru Nofong
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  2. Hamidu Abdel-Fatao
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  3. Michael Kofi Afriyie
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  4. John Wondoh
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Corresponding author

Correspondence to Vincent Mwintieru Nofong .

Editor information

Editors and Affiliations

  1. Division of Information Systems, University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    R. Uday Kiran

  2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Philippe Fournier-Viger

  3. Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain

    Jose M. Luna

  4. Department of Computer Science, Electrical Engineering, and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  5. Department of Computer Science, Ashoka University, Sonepat, Haryana, India

    Anirban Mondal

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Nofong, V.M., Abdel-Fatao, H., Afriyie, M.K., Wondoh, J. (2021). Discovering Self-reliant Periodic Frequent Patterns. In: Kiran, R.U., Fournier-Viger, P., Luna, J.M., Lin, J.CW., Mondal, A. (eds) Periodic Pattern Mining . Springer, Singapore. https://doi.org/10.1007/978-981-16-3964-7_7

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  • DOI: https://doi.org/10.1007/978-981-16-3964-7_7

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