Effective algorithms for mining frequent-utility itemsets

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 FUIM_TWU-Tree (Frequent-utility Itemset Mining based on TWU-Tree) and FUIM_TF-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 FUIM_TWU-Tree and FUIM_TF-Tree perform efficiently in terms of speed, pruning performance and scalability.

KEYWORDS:

• Frequent-utility itemset mining
• frequent itemsets
• high-utility itemsets
• data mining

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).

Additional information

Funding

The work was 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].