.png){kind=icon}
返回
Uncertainty Oriented-Incremental Erasable Pattern Mining Over Data Streams
.png){kind=icon}
PRE
AI
DOI：10.1109/TSMC.2024.3505904.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In a manufacturing factory, product lines are organized by several constituents and exhibit a profit value, i.e., income from products. Erasable patterns are less profitable patterns whose gain, i.e., the sum of product profits, does not exceed a user-defined threshold. Mining erasable patterns provides the necessary information to users who want to increase profits by erasing less profitable patterns. There are requirements for a method which efficiently manages uncertain databases in incremental environments to identify erasable patterns that consider uncertainty. Because our novel technique uses a list structure, it is more efficient at finding erasable patterns from incremental databases. Moreover, accumulated stream data should be handled efficiently to identify new useful patterns in both additional data and the existing data. In this article, an algorithm using a list-based structure is proposed to extract erasable patterns containing valuable knowledge from uncertain databases in real time with effective and productive performance. In order to derive erasable patterns from continuously accumulated stream databases, the structure efficiently manages the information gathered from the previous database. Extensive performance and pattern quality evaluations were conducted using real and synthetic datasets. The results show that the algorithm performs up to seven times faster than state-of-the-art erasable pattern mining algorithms on real datasets and scales adeptly on synthetic datasets while delivering reliable and significant result patterns.
Keyword:
Databases
Data mining
Streams
Uncertainty
Real-time systems
Manufacturing
Data structures
Synthetic data
Runtime
Reliability
Data streams
erasable pattern mining (EPM)
incremental mining
uncertainty
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
ITUFP: A fast method for interactive mining of Top-K frequent patterns from uncertain dataITUFP: 一种快速交互式挖掘不确定数据中Top-K频繁模式的方法
PREAI
分享收藏
分享收藏
Applying uncertain frequent pattern mining to improve ranking of retrieved images.png)
APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏
查看更多内容