Discovering utility-based episode rules in complex event sequences

Abstract

Mining high utility episode rules in complex event sequences has emerged as an important topic in data mining because the utility-based episode rules generated may provide important insights that facilitate decision making for expert and intelligent systems. Although one may employ previous methods in this research area to indirectly construct utility-based episode rules, they typically lack efficiency and effectiveness for real-world applications. In this paper, we develop a novel methodology to directly generate high utility episode rules during the mining process, which is the first work addressing the issue of utility-based episode rule mining. Our goal is to simultaneously resolve the difficulty of the previous reported methods for frequent episode mining and utility-based episode mining. An algorithm called UBER-Mine (Utility-Based Episode Rules) and a structure named UR-Tree (Utility Rule Tree) are proposed to mine efficiently the complete set of high utility episode rules in complex event sequences. In short, UBER-Mine is based on an extended downward closure property, which can efficiently discover utility-based episode rules. On the other hand, UR-Tree can maintain important event information without producing candidate episodes to further accelerate the mining process. Results on both real and synthetic datasets show that UBER-Mine with UR-Tree has good scalability on large datasets and runs faster than the basic UBER-Mine and the current best high utility episode mining algorithm over 100 times. Furthermore, by proposing a high-utility episode-rule model called IV-UBER (InVestment by Utility-Based Episode Rules), we further demonstrate the effectiveness of our method for mining high utility-based episode rules on a real-world application for stock investment. The experimental results show that our proposed IV-UBER method outperforms several state-of-the-art algorithms in terms of both precision and annualized return for investment.