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 {\it frequent patterns} and {\it association rules} from probabilistic data under the {\it 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 {\it bottom-up} and {\it 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.