Mining High-Utility Sequential Patterns in Uncertain Databases

Jerry Chun-Wei Lin, Gautam Srivastava, Yuanfa Li, Tzung-Pei Hong, Shyue-Liang Wang

Abstract: During our research conducted in this paper, we demonstrate a successful mining progress to mine the sequential high-utility patterns of uncertain databases. A PUL-Chain structure is developed and built in this paper with several pruning methods to decrease the search space of required patterns for mining efficiency improvement. In contrast to the standard HUS-Span, our experimental results show clearly that both in runtime as well as in the number of candidates discovered, the developed algorithms showed the effectiveness of the discovered patterns and its mining efficiency compared to the elder HUS-Span model. We present the details of our research here in this paper and also focus our attention to future directions that this research may take in the years to come.