.png){kind=icon}
返回
Significance-based discriminative sequential pattern mining
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2018.12.046.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Discriminative sequential patterns are sub-sequences whose occurrences exhibit significant differences across sequential data sets with different class labels. The discovery of such types of patterns has many practical applications in different fields. To date, various algorithms for mining discriminative sequential patterns have been proposed. However, the reported patterns from these methods usually contain many false positives that only hold in the sample data by chance. To alleviate this issue, we put forward the concept of significance-based discriminative sequential pattern mining and a corresponding algorithm DSPM-MTC (Discriminative Sequential Pattern Mining with Multiple Testing Correction). The key idea of DSPM-MTC is to integrate the multiple hypothesis testing correction procedure into the pattern mining process to generate a pattern set with error rate control. To demonstrate the effectiveness of DSPM-MTC, we conduct a series of experiments on real sequential data sets and simulation data sets. The experimental results show that DSPM-MTC can effectively recognize false discoveries to generate a pattern set with statistical quality control. (C) 2018 Elsevier Ltd. All rights reserved.
Keyword:
Sequential pattern
Discriminative pattern
Multiple hypothesis testing
Family-wise error rate
False discovery rate
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:
2.9W
被引数:
10.2W
机构
引用论文
.png){kind=icon}
Economic Assessment of the Installation of Photovoltaic Panels at the American University of Armenia
PREAI
分享收藏
查看更多内容