Abstract
In recent years, data mining has become one of the most demanding areas of computer science. For instance, it helps to discover frequent patterns by applying intelligence tools, techniques and methodologies from various kinds of databases. However, with the rapid growth of modern technology, high volumes of data with different characteristics are generated by many applications. Situation has become much more challenging and sophisticated when datasets are uncertain in nature and flowing at high velocity. Many applications also demand real-time analysis of data depending on current characteristics. Several researches have been made to mitigate the challenges regarding uncertain data streams. However, as the datasets are streaming in nature, frequent patterns of those datasets may be huge in size, and thus may require further mining to find the interesting patterns. Interestingness of patterns can be measured by associating weight with each item. In this paper, we propose a novel tree-based approach called WFPMUDS (Weighted Frequent Patterns mining from Uncertain Data Streams)-growth, which is capable of capturing recent behavior of uncertain data streams and only produces significant (weighted) patterns.
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References
Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: EDBT 1996, pp. 1–17 (1996)
Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: Mining sequential patterns by pattern-growth: the PrefixSpan approach. IEEE TKDE 16(11), 1424–1440 (2004)
Choi, P., Hwang, B.: Dynamic weighted sequential pattern mining for USN system. In: ACM IMCOM 2017, pp. 19:1–19:6 (2017)
Hsu, K.: Effectively mining time-constrained sequential patterns of smartphone application usage. In: ACM IMCOM 2017, pp. 39:1–39:8 (2017)
Rahman, M.M., Ahmed, C.F., Leung, C.K., Pazdor, A.G.M.: Frequent sequence mining with weight constraints in uncertain databases. In: IMCOM 2018, pp. 48:1–48:8 (2018)
Rasheed, F., Alshalalfa, M., Alhajj, R.: Efficient periodicity mining in time series databases using suffix trees. IEEE TKDE 23(1), 79–94 (2011)
Li, Y., Yeh, J., Chang, C.: Isolated items discarding strategy for discovering high utility itemsets. DKE 64(1), 198–217 (2008)
Gunawan, D., Mambo, M.: Set-valued data anonymization maintaining data utility and data property. In: IMCOM 2018, pp. 88:1–88:8 (2018)
Yun, U.: Efficient mining of weighted interesting patterns with a strong weight and/or support affinity. Inf. Sci. 177(17), 3477–3499 (2007)
Leung, C., Khan, Q.I.: DSTree: a tree structure for the mining of frequent sets from data streams. In: IEEE ICDM 2006, pp. 928–932 (2006)
Leung, C., Mateo, M.F., Brajczuk, D.A.: A tree-based approach for frequent pattern mining from uncertain data. In: PAKDD 2008, pp. 653–661 (2008)
Goodman, S.H.: Cup tree. US Patent D393,783, 28 April 1998
Leung, C., Tanbeer, S.: PUF-tree: a compact tree structure for frequent pattern mining of uncertain data. In: PAKDD 2013, Part I, pp. 13–25 (2013)
Shajib, M., Samiullah, M., Ahmed, C., Leung, C., Pazdor, A.: An efficient approach for mining frequent patterns over uncertain data streams. In: IEEE ICTAI 2016, pp. 980–984 (2016)
Ahmed, C., Tanbeer, S., Jeong, B., Lee, Y.: Handling dynamic weights in weighted frequent pattern mining. IEICE Trans. Inf. Syst. 91(11), 2578–2588 (2008)
Leung, C., Hao, B.: Mining of frequent itemsets from streams of uncertain data. In: IEEE ICDE 2009, pp. 1663–1670 (2009)
Outten, F.W., Djaman, O., Storz, G.: A suf operon requirement for Fe-S cluster assembly during iron starvation in Escherichia coli. Mol. Microbiol. 52(3), 861–872 (2004)
Ahmed, C., Tanbeer, S., Jeong, B., Lee, Y., Choi, H.: Single-pass incremental and interactive mining for weighted frequent patterns. Expert Syst. Appl. 39(9), 7976–7994 (2012)
Ahmed, C., Tanbeer, S., Jeong, B.: Efficient mining of weighted frequent patterns over data streams. In: IEEE HPCC 2009, pp. 400–406 (2009)
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This project is partially supported by NSERC (Canada) and University of Manitoba.
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Ovi, J.A., Ahmed, C.F., Leung, C.K., Pazdor, A.G.M. (2019). Mining Weighted Frequent Patterns from Uncertain Data Streams. In: Lee, S., Ismail, R., Choo, H. (eds) Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication (IMCOM) 2019. IMCOM 2019. Advances in Intelligent Systems and Computing, vol 935. Springer, Cham. https://doi.org/10.1007/978-3-030-19063-7_72
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DOI: https://doi.org/10.1007/978-3-030-19063-7_72
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