CG-FHAUI: an efficient algorithm for simultaneously mining succinct pattern sets of frequent high average utility itemsets

Abstract

The identification of both closed frequent high average utility itemsets (CFHAUIs) and generators of frequent high average utility itemsets (GFHAUIs) has substantial significance because they play an essential and concise role in representing frequent high average utility itemsets (FHAUIs). These concise summaries offer a compact yet crucial overview that can be much smaller. In addition, they allow the generation of non-redundant high average utility association rules, a crucial factor for decision-makers to consider. However, difficulty arises from the complexity of discovering these representations, primarily because the average utility function does not satisfy both monotonic and anti-monotonic properties within each equivalence class, that is for itemsets sharing the same subset of transactions. To tackle this challenge, this paper proposes an innovative method for efficiently extracting CFHAUIs and GFHAUIs. This approach introduces novel bounds on the average utility, including a weak lower bound called \(wlbau\) and a lower bound named \(auvlb\). Efficient pruning strategies are also designed with the aim of early elimination of non-closed and/or non-generator FHAUIs based on the \(wlbau\) and \(auvlb\) bounds, leading to quicker execution and lower memory consumption. Additionally, the paper introduces a novel algorithm, CG-FHAUI, designed to concurrently discover both GFHAUIs and CFHAUIs. Empirical results highlight the superior performance of the proposed algorithm in terms of runtime, memory usage, and scalability when compared to a baseline algorithm.