A predictive GA-based model for closed high-utility itemset mining

Mining patterns with high utilization (or called high-utility itemset mining, HUIM) is considered a major issue in recent decades especially in the market (e.g., supermarket) engineering since it reveals the profitable information/products for decision-making. Many existing works focused on mining high-utility itemsets from databases that revealed a very large amount of patterns. This process cannot make the precise decision in a short time, e.g., real-time and online decision-making system, since it is not a trivial task to find the appropriate and useful information from a huge amount of the discovered knowledge in a limited time. Mining closed patterns with high utilization (or called closed high-utility pattern mining) is an alternative way to reveal fewer and concise patterns with high utilization in market engineering. However, many past works considered the complete mining progress of all HUIs and they do not consider the correlation between transactions thus when the transactions are not highly relevant, the trained model could not be perfectly used for the predication, which shows inappropriate results in machine learning tasks. In this paper, we consider the clustering models that can divide the transactions into the proper groups based on their correlation, which can be used to make a better accuracy model for prediction. To reduce the mining progress of the CHUIs, a compact GA model is also used in the mining progress, thus in the large-scale situation, a sufficient number of satisfied CHUIs can be discovered in a limited time. Based on the designed framework, the designed model can, not only mine the good enough CHUIs in the limited time for the large-scale environment but also the adopted clustering concept can make a better predictive model in the machine learning process, thus higher accuracy results can be obtained. Experimental results are then evaluated to compare the state-of-the-art CHUI-Miner and CLS-Miner with the developed DcGA model, and the designed DcGA achieves the best runtime performance and satisfied mining results. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study explores the importance of mining high-utilization patterns in market engineering and proposes a new approach based on clustering models and a compact genetic algorithm to discover high-utility closed patterns more quickly and accurately. Experimental results show that this method outperforms existing methods in terms of runtime performance and mining results.