.png){kind=icon}
返回
Mining inter-sequence patterns
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2008.10.008.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Sequential pattern and inter-transaction pattern mining have long been important issues in data mining research. The former finds sequential patterns Without considering the relationships between transactions in databases, while the latter finds inter-transaction patterns without considering the ordered relationships of items within each transaction. However, if we want to find patterns that cross transactions in a sequence database, called inter-sequence patterns, neither of the above models can perform the task. In this paper, we propose a new data mining model for mining frequent inter-sequence patterns. We design two algorithms, M-Apriori and EISP-Miner. to find such patterns. The former is an Apriori-like algorithm that can mine inter-sequence patterns, but it is not efficient. The latter, a new method that we propose, employs several mechanisms for mining inter-sequence patterns efficiently. Experiments show that EISP-Miner is very efficient and outperforms M-Apriori by several orders of magnitude. (C) 2008 Elsevier Ltd. All rights reserved.
Keyword:
Data mining
Inter-sequence pattern
Inter-transaction pattern
Sequential pattern
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:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
.png){kind=icon}
查看更多内容