Abstract: Recently, with the growing popularity of Internet of Things (IoT) and pervasive computing, a large amount of uncertain data, e.g. RFID data, sensor data, real-time video data, etc., has been collected. As one of the most fundamental issues of uncertain data mining, uncertain frequent pattern mining has attracted much attention in the database and the data mining communities. Although there have been some solutions for uncertain frequent pattern mining, most of them assume that the data is independent, which is not true in most real-world scenarios. Therefore, current methods that are based on the independent assumption may generate inaccurate results for correlated uncertain data. In this paper, we focus on the problem of mining frequent itemsets over correlated uncertain data, where correlation can exist in any pair of uncertain data objects (transactions). We propose a novel probabilistic model, called correlated frequent probability model (CFP model) to represent the probability distribution of support in a given correlated uncertain data set. Based on the distribution of support derived from the CFP model, we observe that some probabilistic frequent itemsets are only frequent in several transactions with high positive correlation. In particular, the itemsets, which are global probabilistic frequent, have more significance in eliminating the influence of the existing noise and correlation in data. In order to reduce redundant frequent itemsets, we further propose a new type of patterns, called global probabilistic frequent itemsets, to identify itemsets that are always frequent in each group of transactions if the whole correlated uncertain database is divided into disjoint groups based on their correlation. To speed up the mining process, we also design a dynamic programming solution, as well as two pruning and bounding techniques. Extensive experiments on both real and synthetic datasets verify the effectiveness and efficiency of the proposed model and algorithms.