High volumes of wide varieties of valuable data of different veracity (e.g., imprecise and uncertain data) can be easily generated or collected at a high velocity for various knowledge-based and intelligent information & engineering systems in many real-life situations. Embedded in these big data is valuable knowledge and useful information, which can be discovered by data science solutions. As a popular data science task, frequent pattern mining aims to discover implicit, previously unknown and potentially useful information and valuable knowledge in terms of sets of frequently co-occurring items. Many of the existing frequent pattern mining algorithms use a transaction-centric mining approach to find frequent patterns from precise data. However, there are situations in which an item-centric mining approach is more appropriate, and there are also situations in which data are imprecise and uncertain. In this article, we present an item-centric algorithm for mining frequent patterns from big uncertain data. Evaluation results show the effectiveness of our algorithm in item-centric mining of frequent patterns from big uncertain data.