Abstract
We study the problem of mining frequent itemsets from uncertain data under a probabilistic model. We consider transactions whose items are associated with existential probabilities. A decremental pruning (DP) technique, which exploits the statistical properties of items’ existential probabilities, is proposed. Experimental results show that DP can achieve significant computational cost savings compared with existing approaches, such as U-Apriori and LGS-Trimming. Also, unlike LGS-Trimming, DP does not require a user-specified trimming threshold and its performance is relatively insensitive to the population of low-probability items in the dataset.
This research is supported by Hong Kong Research Grants Council Grant HKU 7134/06E.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proc. of 20th ICDE, pp. 487–499. Morgan Kaufmann, San Francisco (1994)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proc. of the 11th ICDE, pp. 3–14. IEEE Computer Society Press, Los Alamitos (1995)
Brossette, S.E., Sprague, A.P., Hardin, J.M., Jones, W.T., Moser, S.A.: Association rules and data mining in hospital infection control and public health surveillance. Journal of the American Medical Informatics Association, 373–381 (1998)
Chui, C.K., Kao, B., Hung, E.: Mining frequent itemsets from uncertain data. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 47–58. Springer, Heidelberg (2007)
Zimányi, E., Pirotte, A.: Imperfect information in relational databases. In: Uncertainty Management in Information Systems, pp. 35–88 (1996)
Bayardo Jr., R.J.: Efficiently mining long patterns from databases. In: Proc. of SIGMOD 1998, pp. 85–93. ACM Press, New York (1998)
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Chui, CK., Kao, B. (2008). A Decremental Approach for Mining Frequent Itemsets from Uncertain Data. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_8
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DOI: https://doi.org/10.1007/978-3-540-68125-0_8
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