Abstract
In this uncertain world, data uncertainty is inherent in many applications and its importance is growing drastically due to the rapid development of modern technologies. Nowadays, researchers have paid more attention to mine patterns in uncertain databases. A few recent works attempt to mine frequent uncertain sequential patterns. Despite their success, they are incompetent to reduce the number of false-positive pattern generation in their mining process and maintain the patterns efficiently. In this paper, we propose multiple theoretically tightened pruning upper bounds that remarkably reduce the mining space. A novel hierarchical structure is introduced to maintain the patterns in a space-efficient way. Afterward, we develop a versatile framework for mining uncertain sequential patterns that can effectively handle weight constraints as well. Besides, with the advent of incremental uncertain databases, existing works are not scalable. There exist several incremental sequential pattern mining algorithms, but they are limited to mine in precise databases. Therefore, we propose a new technique to adapt our framework to mine patterns when the database is incremental. Finally, we conduct extensive experiments on several real-life datasets and show the efficacy of our framework in different applications.
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Notes
- 1.
uWSequence[12] defines the upper bound of expected support as \(expSupport^{top}(\alpha )\) = \(maxPr(\alpha _{m-1})\times maxPr(i_{m}) \times sup_{i_{m}}\) where \(sup_{i_{m}}\) is the support count of \(i_{m}\).
- 2.
- 3.
For the Retail market-basket dataset, we used the first one-fifth transactions (1st month) as the initial portion and then 4 increments to represent the next 4Â months.
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This work is partially supported by NSERC (Canada) and University of Manitoba.
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Roy, K.K., Moon, M.H.H., Rahman, M.M., Ahmed, C.F., Leung, C.K. (2021). Mining Sequential Patterns in Uncertain Databases Using Hierarchical Index Structure. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12713. Springer, Cham. https://doi.org/10.1007/978-3-030-75765-6_3
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