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The problem of high utility sequence mining (HUSM) in quantitative sequence databases (QSDBs) is more general than that of mining frequent sequences in sequence databases. An important limitation of HUSM is that a user-predefined minimum utility threshold is used to decide if a sequence is high utility. However, this is not suitable for many real-life applications as sequences may differ in importance. Another limitation of HUSM is that data in QSDBs are assumed to be precise. But in the real world, data collected by sensors, or other means, may be uncertain. Thus, this paper proposes a framework for mining high utility-probability sequences (HUPSs) in uncertain QSDBs (UQSDBs) with multiple minimum utility thresholds using a minimum utility. Two new width and depth pruning strategies are also introduced to eliminate low utility or low probability sequences as well as their extensions early, and to reduce the sets of candidate items for extensions during the mining process. Based on these strategies, a novel efficient algorithm named HUPSMT is designed for discovering HUPSs. Finally, an experimental study conducted with both real-life and synthetic UQSDBs shows the performance of HUPSMT in terms of time and memory consumption.

HUPSMT: AN EFFICIENT ALGORITHM FOR MINING HIGH UTILITY-PROBABILITY SEQUENCES IN UNCERTAIN DATABASES WITH MULTIPLE MINIMUM UTILITY THRESHOLDS