Mining top-k frequent patterns from uncertain databases

Mining uncertain frequent patterns (UFPs) from uncertain databases was recently introduced, and there are various approaches to solve this problem in the last decade. However, systems are often faced with the problem of too many UFPs being discovered by the traditional approaches to this issue, and thus will spend a lot of time and resources to rank and find the most promising patterns. Therefore, this paper introduces a task named mining top-k UFPs from uncertain databases. We then propose an efficient method named TUFP (mining Top-k UFPs) to carry this out. Effective threshold raising strategies are introduced to help the proposed algorithm reduce the number of generated candidates to enhance the performance in terms of the runtime as well as memory usage. Finally, several experiments on the number of generated candidates, mining time, memory usage and scalability of TUFP and two state-of-the-art approaches (CUFP-mine and LUNA) were conducted. The performance studies show that TUFP is efficient in terms of mining time, memory usage and scalability for mining top-k UFPs.