Utility-Driven Mining of Trend Information for Intelligent System

TL;DR

This paper introduces a novel utility-driven framework and algorithms for mining recent high-utility patterns in temporal databases, enabling up-to-date insights for intelligent systems with high efficiency and low memory usage.

Contribution

It proposes a new framework and algorithms for discovering recent high-utility patterns considering recency, utility thresholds, and efficient data structures, addressing limitations of traditional utility mining methods.

Findings

The RUP algorithm efficiently identifies recent high-utility patterns in large databases.

The improved algorithms with pruning strategies outperform baseline methods in speed.

Experimental results confirm the effectiveness and efficiency of the proposed approach.

Abstract

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.