
MINING OF HIGH-UTILITY ITEMSETS WITH NEGATIVE UTILITY
Author:
Tung N.T, Nguyen Le Van, Trinh Cong Nhut, Tran Van Sang
This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
The goal of the high-utility itemset mining task is to discover combinations of items that yield high profits from transactional databases. HUIM is a useful tool for retail stores to analyze customer behaviors. However, in the real world, items are found with both positive and negative utility values. To address this issue, we propose an algorithm named Modified Efficient High‐utility Itemsets mining with Negative utility (MEHIN) to find all HUIs with negative utility. This algorithm is an improved version of the EHIN algorithm. MEHIN utilizes 2 new upper bounds for pruning, named revised subtree and revised local utility. To reduce dataset scans, the proposed algorithm uses transaction merging and dataset projection techniques. An array‐based utility‐counting technique is also utilized to calculate upper‐bound efficiently. The MEHIN employs a novel structure called P-set to reduce the number of transaction scans and to speed up the mining process. Experimental results show that the proposed algorithms considerably outperform the state-of-the-art HUI-mining algorithms on negative utility in retail databases in terms of runtime.
Pages | 44-47 |
Year | 2021 |
Issue | 2 |
Volume | 1 |