Abstract
Discovering periodic itemsets in transaction databases is an emerging data mining task. However, current algorithms are designed to discover periodic itemsets in a single sequence. But in real-life, it is desirable to find periodic patterns that are common to multiple sequences. For example, a retail store manager can benefit from finding that many customers buy the same products every week in a retail store, to adapt its marketing and sale strategies. To address this drawback of previous work, this paper defines the problem of mining periodic patterns common to multiple sequences and proposes an efficient algorithm named MPFPS, which relies on a novel PFPS-list structure and two novel periodicity measures to assess periodicity with more flexibility. Experiments on several synthetic and real-life databases show that MPFPS is efficient and can filter many non-periodic itemsets to reveal the desired patterns.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agrawal, R., Srikant, R.: Mining sequential patterns. In: 11th International Conference on Data Engineering, pp. 3–14. IEEE Press, Taipei (1995)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: 20th International Conference on Very Large Data Bases, pp. 487–499. Morgan Kaufmann, Santiago de Chile (1994)
Aggarwal, C.C., Han, J.: Frequent Pattern Mining. Springer, Switzerland (2014). https://doi.org/10.1007/978-3-319-07821-2
Zaki, M.J., Gouda, K.: Fast vertical mining using diffsets. In: 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 326–335. ACM, Washington (2003)
Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., Hsu, M.: PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 355–359. ACM, Boston (2000)
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_22
Fournier-Viger, P., Gomariz, A., Gueniche, T., Soltani, A., Wu, C., Tseng, V.S.: SPMF: a Java open-source pattern mining library. J. Mach. Learn. Res. 15(1), 3389–3393 (2014)
Fournier-Viger, P., Lin, J.C.-W., Duong, Q.-H., Dam, T.-L.: PHM: mining periodic high-utility itemsets. In: Perner, P. (ed.) ICDM 2016. LNCS (LNAI), vol. 9728, pp. 64–79. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41561-1_6
Fournier-Viger, P., Lin, C.-W., Duong, Q.-H., Dam, T.-L., Sevcic, L., Uhrin, D., Voznak, M.: PFPM: discovering Periodic Frequent Patterns with Novel Periodicity Measures. In: 2nd Czech-China Scientific Conference, pp. 1–13 (2017)
Fournier-Viger, P., Lin, J.C.-W., Vo, B, Chi, T.T., Zhang, J., Le, H.B.: A survey of itemset mining. J. WIREs Data Mining Knowl. Discov. 7(4) (2017)
Kiran, R.U., Reddy, P.K.: Mining rare periodic-frequent patterns using multiple minimum supports. In: 15th International Conference on Management of Data, pp. 7–8. IEEE, Mysore (2009)
Kiran, R.U., Kitsuregawa, M., Reddy, P.K.: Efficient discovery of periodic-frequent patterns in very large databases. J. Syst. Softw. 112, 110–121 (2016)
Amphawan, K., Lenca, P., Surarerks, A.: Mining Top-K periodic-frequent pattern from transactional databases without support threshold. In: Papasratorn, B., Chutimaskul, W., Porkaew, K., Vanijja, V. (eds.) IAIT 2009. CCIS, vol. 55, pp. 18–29. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10392-6_3
Fan, Y., Ye, Y., Chen, L.: Malicious sequential pattern mining for automatic malware detection. Expert Syst. Appl. 52, 16–25 (2016)
Halder, S., Samiullah, M., Lee, Y.-K.: Supergraph based periodic pattern mining in dynamic social networks. Expert Syst. Appl. 72, 430–442 (2017)
Dinh, T., Huynh, V.-N., Le, B.: Mining periodic high utility sequential patterns. In: Nguyen, N.T., Tojo, S., Nguyen, L.M., Trawiński, B. (eds.) ACIIDS 2017. LNCS (LNAI), vol. 10191, pp. 545–555. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54472-4_51
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Fournier-Viger, P., Li, Z., Lin, J.CW., Kiran, R.U., Fujita, H. (2018). Discovering Periodic Patterns Common to Multiple Sequences. In: Ordonez, C., Bellatreche, L. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2018. Lecture Notes in Computer Science(), vol 11031. Springer, Cham. https://doi.org/10.1007/978-3-319-98539-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-98539-8_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98538-1
Online ISBN: 978-3-319-98539-8
eBook Packages: Computer ScienceComputer Science (R0)