Abstract
To mine frequent itemsets from uncertain data, many existing algorithms rely on expected support based mining. An alternative approach relies on probabilistic based mining, which captures the frequentness probability. While the possible world semantics are widely used, the exponential growth of possible worlds makes the probabilistic based mining computationally challenging when compared to the expected support based mining. In this paper, we propose two efficient approximate hyperlinked structure based algorithms, which generate a collection of all potentially probabilistic frequent itemsets with a novel upper bound and verify if they are truly probabilistic frequent. Experimental results show the efficiency of our algorithms in mining probabilistic frequent itemsets from uncertain data.
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Tong, W., Leung, C.K., Liu, D., Yu, J. (2015). Probabilistic Frequent Pattern Mining by PUH-Mine. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9313. Springer, Cham. https://doi.org/10.1007/978-3-319-25255-1_63
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DOI: https://doi.org/10.1007/978-3-319-25255-1_63
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