Abstract
Periodic-frequent pattern mining involves finding all periodically occurring patterns in a temporal database. Most previous studies found these patterns by storing the temporal occurrence information of an item in a list structure. Unfortunately, this approach makes pattern mining computationally expensive on dense databases due to increased list sizes. With this motivation, this paper explores the concept of complements, and proposes an efficient algorithm that records non-occurrence information of an item to find all desired patterns in a dense database. Experimental results demonstrate that our algorithm is efficient.
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Notes
- 1.
The term set difference refers to relative complement, whereas the term complement typically refers to absolute complement.
References
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD, pp. 207–216 (1993)
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
Anirudh, A., Kiran, R.U., Reddy, P.K., Kitsuregawa, M.: Memory efficient mining of periodic-frequent patterns in transactional databases. In: 2016 IEEE Symposium Series on Computational Intelligence, pp. 1–8 (2016)
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_25
Luna, J.M., Fournier-Viger, P., Ventura, S.: Frequent itemset mining: a 25 years review. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 9(6), e1329 (2019)
Ravikumar, P., Likhitha, P., Venus Vikranth Raj, B., Uday Kiran, R., Watanobe, Y., Zettsu, K.: Efficient discovery of periodic-frequent patterns in columnar temporal databases. Electronics 10(12), 1478 (2021)
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_24
Zaki, M.J., Gouda, K.: Fast vertical mining using diffsets. In: SIG KDD, pp. 326–335 (2003)
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Veena, P. et al. (2022). Towards Efficient Discovery of Periodic-Frequent Patterns in Dense Temporal Databases Using Complements. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13427. Springer, Cham. https://doi.org/10.1007/978-3-031-12426-6_16
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DOI: https://doi.org/10.1007/978-3-031-12426-6_16
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