ABSTRACT
The current pattern mining algorithms focus on discovering either frequent itemsets or high-utility itemsets. The goal of this research is to study the problem of mining frequent-utility itemsets. To solve this problem, two novel algorithms named FUIMTWU-Tree (Frequent-utility Itemset Mining based on TWU-Tree) and FUIMTF-Tree (Frequent-utility Itemset Mining based on TF-Tree) are presented based on the integration of IHUP and HUI-Miner. The TWU-tree and TF-Tree structures are utilised to avoid the unnecessary utility-list construction of itemsets that do not appear in a transaction dataset. The performance of the proposed algorithms is evaluated on various datasets. The results of the experiments demonstrate that FUIMTWU-Tree and FUIMTF-Tree perform efficiently in terms of speed, pruning performance and scalability.
Acknowledgements
This work is supported by the Zhejiang Provincial Natural Science Foundation of China (LQ21F030010); Ningbo Natural Science Foundation of China (202003N4306); General Project of Education Department of Zhejiang Province (Y202044193, Y202044208); the Public Welfare Foundation of Ningbo (2021S108); Ningbo Science and Technology Special Projects of China (2021Z019).
Disclosure statement
No potential conflict of interest was reported by the author(s).