High-Utility Pattern Mining via Improved Pruning Strategy and New Thresholding Strategy

Abstract

High-Utility Itemset Mining (HUIM) incorporates both the profit and profitability factors, a critical component of data mining. Nevertheless, the majority of HUIM algorithms are developed to operate on a single machine, which proves inefficient for big data due to limited memory and processing resources. This paper proposes a novel Coverage, Unit profit utility-based High Utility patterns from Certain data (CUHUC) framework. The proposed framework follows two stages: High Utility Mining and Pruning, with a novel thresholding strategy. In high utility mining, the uncertain dataset is converted into a certain dataset. Certain factors like Unit profit, Purchase quantity, and Coverage-based High Utility Itemset (UPC-HUI) are generated from the dataset. Further, the generated list is then pruned to eliminate unpromising candidates. The remaining promising items in a fixed order are arrangeds. An innovative Improved Pruning with Novel Thresholding Strategy (IPNTS) is proposed to remove the unpromising candidates. This can set a minimum utility value to filter the significant pattern and propose the Enhanced Secretary Bird Optimisation (ESBO) algorithm to obtain the optimal minimum utility. The ESBO scheme attained a runtime of 42.412 s, memory usage of 196 MB, and cost rate of 0.49500, signifying improved performance compared to conventional methods.