Towards episode rules with non-overlapping frequency and targeted mining

Abstract

Data mining has become a popular task due to the explosion of rich data. Frequent episode mining (FEM) is an effective technique for extracting valuable and crucial information from event sequences, playing a significant role in various fields such as market basket analysis, association analysis, and management sciences. A variety of FEM algorithms discover frequent episode rules using the frequency function and anti-monotony. Non-overlapping frequency works well for mining episode rules because it cuts down on unnecessary computations and still follows anti-monotonicity. Leveraging this technique, we propose the NONEPI+ algorithm for discovering rules that were previously overlooked. However, there are no algorithms that can be used to discover episode rules that contain target query rules. To fill the research gap in episode rule mining algorithms, we further propose a novel algorithm called TaER for targeted mining of episode rules, i.e., a set of rules containing specific query rules. TaER can successfully discover the episode rules that the users are more specifically interested in. It can provide direction for prediction tasks in many aspects, such as weather observation, network intrusion, e-commerce, and financial behavior prediction. All source code and datasets are available on GitHub https://github.com/DSI-Lab1/TaER.