Abstract
The mining of weighted uncertain interesting patterns (WUIPs) is an interesting problem with many practical applications. In this study, we propose an effective method for mining WUIPs from uncertain databases. We first introduce the TPN-list structure, an extended version of the N-list structure, which is used to represent and discover the WUIPs. A TPN-list intersection algorithm is then developed, which has linear complexity and the ability to self-reduce its size. Several theorems are also proposed for the fast calculation and determination of a WUIP based on its TPN-list. Finally, we propose the HWUIPM algorithm, based on the above proposals, for mining WUIPs from uncertain databases. Our experimental results demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms for mining WUIPs in terms of running time, resource usage and scalability.
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