@article{ref1,
title="Key cause chains selection of electric personal accidents based on combinatorial association analysis model",
journal="China safety science journal (CSSJ)",
year="2022",
author="Zhao, C. and Mi, C. and Zhou, Z. and Fang, J.",
volume="32",
number="4",
pages="99-106",
abstract="In order to improve security management ability and production safety of power enterprises, with 208 electric personal accidents in China from 2015 to 2019 as samples, their occurrence path and inherent cause law were explored. Firstly, a screening model of key cause chains based on association rule mining and grey association analysis was proposed considering the two methods' defects, namely the former was difficult to determine thresholds and the latter can not find hidden connection of data. Then, empirical analysis was conducted, strong association rules among accident causes were discovered by using association rules, and pairwise causes which could reflect accident paths were selected by increasing threshold of grey relational degree to identify key cause chains. Finally, the similarity between these key chains and accident samples was verified by Jaccard similarity calculation method. The results show that they are highly similar, which meanwhile demonstrates the effectiveness of the proposed model. © 2020 China Safety Science Journal. All rights reserved.<p /><p>Language: zh</p>",
language="zh",
issn="1003-3033",
doi="10.16265/j.cnki.issn1003-3033.2022.04.015",
url="http://dx.doi.org/10.16265/j.cnki.issn1003-3033.2022.04.015"
}