.png){kind=icon}
返回
Enabling knowledge discovery through low utility itemset mining
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2024.125955.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
While high utility mining aims to identify patterns that maximize profits, low utility mining (LUIM) focuses on uncovering exceptional patterns, enabling proactive interventions before issues escalate. Existing high utility itemset mining methods use upper-bound pruning to reduce the search space. However, this approach is ineffective for LUIM, as it inadvertently eliminates low utility itemsets (LUIs). In addition, LUIM struggles with distinguishing between LUIs and zero-utility candidates. These challenges in LUIM can be summarized as follows: (1) reducing the search space of LUIM, and (2) distinguishing genuine LUIs from zero-utility candidates. To address these issues, we propose LUIMiner, an algorithm designed to accurately and efficiently find the complete set of LUIs. LUIMiner incorporates two lower-bound pruning strategies - depth and width - to reduce search space and streamline the mining process. We also introduce a redesigned search tree that processes candidates from long to short, enabling effective lower-bound-based pruning. Additionally, a preprocessing phase filters out zero-utility itemsets early in the process by calculation of maximal non- mutually contained itemsets. The Bgenerator structure uses bitwise operations to accelerate computations and reduce database scans. Experimental comparisons with state-of-the-art LUG-Miner & LUIMA demonstrate the effectiveness and efficiency of our algorithm.
Keyword:
Pattern mining
Low utility
Pruning strategy
Lower bound
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容