Efficient Mining of High Average-Utility Sequential Patterns from Uncertain Databases
Jerry Chun‐Wei Lin, Jimmy Ming‐Tai Wu, Philippe Fournier‐Viger, Tzung‐Pei Hong, Ting Li · 2019
In this paper, we address the limitation for mining of high-utility sequential-pattern mining from uncertain databases and present a probabilistic high average-utility sequential pattern mining framework for discovering the set of probabilistic high average-utility sequential patterns from uncertain databases. A level-wise algorithm and three pruning strategies are introduced to mine the set of the desired patterns. Several experiments are then evaluated to show that the proposed algorithm achieves promising performance.