Utility-Driven Mining of Trend Information for Intelligent System

Abstract

Useful knowledge, embedded in a database, is likely to change over time. Identifying the recent changes in temporal data 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 article, we address this issue by introducing a novel framework, named utility-driven mining of recent/trend high-utility patterns (RUPs), 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 high-utility patterns. 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 HUPs in large-scale databases, and that the improved algorithm performs best.