document: Mining uncertain data with probabilistic guarantees 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 effcient 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 .
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