Efficient mining of concise and informative representations of frequent high utility itemsets

Abstract

The discovery of frequent closed high utility itemsets (FCHUIs) and frequent generators of high utility itemsets (FGHUIs) is significant because they serve as important and concise representations of frequent high utility itemsets (FHUIs), offering a brief but essential summary that can be considerably smaller. Besides, they facilitate the generation of nonredundant high utility association rules that are crucial for decision-makers. However, the challenge lies in the difficulty of mining these representations due to scalability issues, high memory usage, and long runtimes, particularly when dealing with dense and large datasets. To address this issue, this paper proposes a novel approach for efficiently mining FCHUIs and FGHUIs using a novel weak lower bound named wlbu on the utility. The approach includes effective pruning strategies for early eliminating non-closed and/or non-generator high utility branches in the prefix search tree based on wlbu. These pruning strategies allow faster execution with lower memory usage. In addition, the paper presents two novel algorithms, FCGHUI-Miner and FGHUI-Miner, which can simultaneously discover both FGHUIs and FCHUIs or solely mine FGHUIs, respectively. The experimental results demonstrate that the proposed algorithms outperform state-of-the-art algorithms in terms of efficiency and effectiveness.