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A related degree-based frequent pattern mining algorithm for railway fault data

Jiaxu Guo Ding Ding Peihan Yang Qi Zou Yaping Huang

高速铁路（英文）2024，Vol.2Issue(2)：101-109,9.

高速铁路（英文）2024，Vol.2Issue(2)：101-109,9.DOI:10.1016/j.hspr.2024.05.003

A related degree-based frequent pattern mining algorithm for railway fault data

Jiaxu Guo ¹Ding Ding ²Peihan Yang ¹Qi Zou ¹Yaping Huang¹

作者信息

1. School of Computer Science and Technology,Beijing Jiaotong University,Beijing 100044,China
2. School of Computer Science and Technology,Beijing Jiaotong University,Beijing 100044,China||Key Laboratory of Big Data & Artificial Intelligence in Transportation,Ministry of Education,Beijing 100044,China
折叠

摘要

关键词

High utility/Quantitative/Frequent pattern mining/Related degree pruning/Fixed pattern length

Key words

High utility/Quantitative/Frequent pattern mining/Related degree pruning/Fixed pattern length

引用本文复制引用

Jiaxu Guo,Ding Ding,Peihan Yang,Qi Zou,Yaping Huang..A related degree-based frequent pattern mining algorithm for railway fault data[J].高速铁路（英文）,2024,2(2):101-109,9.

基金项目

This work was supported by the Research on Key Technologies and Typical Applications of Big Data in Railway Production and Operation(P2023S006)and the Fundamental Research Funds for the Central Universities(2022JBZY023). （P2023S006）

高速铁路（英文）

ISSN：2949-8678

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