.png){kind=icon}
返回
Scalable algorithms for association mining
.png){kind=icon}
PRE
AI
DOI：10.1109/69.846291.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Association rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent itemsets and then, forming conditional implication rules among them. In this paper. we present efficient algorithms for the discovery of frequent itemsets which forms the compute intensive phase of the task. The algorithms utilize the structural properties of frequent itemsets to facilitate fast discovery. The items are organized into a subset lattice search space, which is decomposed into small independent chunks or sublattices, which can be solved in memory. Efficient lattice traversal techniques are presented which quickly identify all the long frequent itemsets and their subsets if required. We also present the effect of using different database layout schemes combined with the proposed decomposition and traversal techniques. We experimentally compare the new algorithms against the previous approaches, obtaining improvements of more than an order of magnitude for our test databases.
Keyword:
association rules
frequent itemsets
equivalence classes
maximal cliques
lattices
data mining
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
暂无机构信息
引用论文
Point-of-Care Prostate Specific Antigen Testing: Examining Translational Progress toward Clinical Implementation.png)
ACS Sensors
OAAI
ACS SensorsIF0
OAAI分享收藏Semi-analytic contact technique in a non-linear parametric model order reduction method for gear simulationsMeccanica
MeccanicaIF0
PREAI
分享收藏
查看更多内容