  Useful knowledge, embedded in a database, is likely to change over time.
Identifying recent changes in temporal databases can provide valuable
up-to-date information to decision-makers. Nevertheless, techniques for mining
high-utility patterns (HUPs) seldom consider recency as a criterion to discover
patterns. Thus, the traditional utility mining framework is inadequate for
obtaining up-to-date insights about real world data. In this paper, we address
this issue by introducing a novel framework, named utility-driven mining of
Recent/trend high-Utility Patterns (RUP) in temporal databases for intelligent
systems, based on user-specified minimum recency and minimum utility
thresholds. The utility-driven RUP algorithm is based on novel global and
conditional downward closure properties, and a recency-utility tree. Moreover,
it adopts a vertical compact recency-utility list structure to store the
information required by the mining process. The developed RUP algorithm
recursively discovers recent HUPs. It is also fast and consumes a small amount
of memory due to its pattern discovery approach that does not generate
candidates. Two improved versions of the algorithm with additional pruning
strategies are also designed to speed up the discovery of patterns by reducing
the search space. Results of a substantial experimental evaluation show that
the proposed algorithm can efficiently identify all recent high-utility
patterns in large-scale databases, and that the improved algorithm performs
best.
