Return
Fast algorithms for mining maximal erasable patterns
DOI:10.1016/j.eswa.2019.01.034.png)
Abstract
En 中文
Since the problem of mining erasable itemsets was identified in 2009, many algorithms have been proposed to improve mining time and/or memory usage. However, algorithms for mining maximal erasable itemsets (MaxEIs) have not been developed, and this article therefore focuses on this problem. Firstly, a GenMax-based algorithm (GenMax-EI) is developed as a baseline algorithm. Secondly, a proposition is developed for fast checking of whether or not an erasable itemset is maximal, and based on this proposition, we develop an algorithm entitled Flag-GenMax-EI for the fast mining of MEIs. Finally, a second proposition for the fast pruning of non-MaxEIs is also developed; based on this proposition, we propose an algorithm entitled PE-GenMax-EI for mining MaxEIs. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Data mining
Erasable itemset
Maximal erasable itemset
Pruning strategy
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
3.0W
Citations:
10.2W
Organization
Cited Papers
EIFDD: An efficient approach for erasable itemset mining of very dense datasets
APPLIED INTELLIGENCE
IF3.5

