UPER: Efficient Utility-driven Partially-ordered Episode Rule Mining

TL;DR

This paper introduces UPER, an efficient algorithm for mining high-utility partially-ordered episode rules in sequential data, enhancing the discovery of valuable event relationships with pruning strategies.

Contribution

The paper proposes UPER, a novel algorithm that efficiently mines high-utility partially-ordered episode rules using a new data structure and pruning techniques.

Findings

UPER effectively discovers high-utility rules in real datasets.

Pruning strategies significantly reduce computational complexity.

Experiments demonstrate UPER's superior performance over baseline methods.

Abstract

Episode mining is a fundamental problem in analyzing a sequence of numerous events. For discovering strong relationships between events in a complex event sequence, episode rule mining is proposed. However, both the episode and episode rules have strict requirements for the order of events. Hence, partially-ordered episode rule mining (POERM) is designed to loosen the constraints on the ordering, i.e., events in the antecedents and consequents of the rule can be unordered, and POERM has been applied to real-life event prediction. In this paper, we consider the utility of POERM, intending to discover more valuable rules. We define the utility of POERs and propose an algorithm called UPER to discover high-utility partially-ordered episode rules. In addition, we adopt a data structure named NoList to store the necessary information, analyze the expansion of POERs in detail, and propose several pruning strategies (namely WEUP, REUCSP, and REEUP) to reduce the number of candidate rules. Finally, we conduct experiments on several datasets to demonstrate the effectivene