2010 MiningUncertainDatawithProbabil

• (Sun et al., 2010) ⇒ Liwen Sun, Reynold Cheng, David W. Cheung, and Jiefeng Cheng. (2010). “Mining Uncertain Data with Probabilistic Guarantees.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835841

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• http://scholar.google.com/scholar?q=%22Mining+uncertain+data+with+probabilistic+guarantees%22+2010
• http://portal.acm.org/citation.cfm?id=1835841&preflayout=flat#citedby

Quotes

Abstract

Data uncertainty is inherent in applications such as sensor monitoring systems, location-based services, and biological databases. To manage this vast amount of imprecise information, probabilistic databases have been recently developed. In this paper, we study the discovery of frequent patterns and association rules from probabilistic data under the Possible World Semantics. This is technically challenging, since a probabilistic database can have an exponential number of possible worlds. We propose two efficient algorithms, which discover frequent patterns in bottom-up and top-down manners. Both algorithms can be easily extended to discover maximal frequent patterns. We also explain how to use these patterns to generate association rules. Extensive experiments, using real and synthetic datasets, were conducted to validate the performance of our methods.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 MiningUncertainDatawithProbabil	Liwen Sun Reynold Cheng David W. Cheung Jiefeng Cheng			Mining Uncertain Data with Probabilistic Guarantees		KDD-2010 Proceedings		10.1145/1835804.1835841		2010