Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining

In the process of gas prediction and early warning, outliers in the data series are often discarded. There is also a likelihood of missing key information in the analysis process. To this end, this paper proposes an early warning model of coal face gas multifactor coupling relationship analysis. The...

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Autores principales:	Yuxin Huang, Jingdao Fan, Zhenguo Yan, Shugang Li, Yanping Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	apriori algorithm association rules <i>k</i>-means algorithm outlier detection gas risks warning Technology T
Acceso en línea:	https://doaj.org/article/06881430ed074278856e993e62007a8c
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spelling	oai:doaj.org-article:06881430ed074278856e993e62007a8c2021-11-11T15:44:05ZResearch on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining10.3390/en142168891996-1073https://doaj.org/article/06881430ed074278856e993e62007a8c2021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/6889https://doaj.org/toc/1996-1073In the process of gas prediction and early warning, outliers in the data series are often discarded. There is also a likelihood of missing key information in the analysis process. To this end, this paper proposes an early warning model of coal face gas multifactor coupling relationship analysis. The model contains the <i>k</i>-means algorithm based on initial cluster center optimization and an Apriori algorithm based on weight optimization. Optimizing the initial cluster center of all data is achieved using the cluster center of the preorder data subset, so as to optimize the <i>k</i>-means algorithm. The optimized algorithm is used to filter out the outliers in the collected data set to obtain the data set of outliers. Then, the Apriori algorithm is optimized so that it can identify more important information that appears less frequently in the events. It is also used to mine and analyze the association rules of abnormal values and obtain interesting association rule events among the gas outliers in different dimensions. Finally, four warning levels of gas risk are set according to different confidence intervals, the truth and reliable warning results are obtained. By mining association rules between abnormal data in different dimensions, the validity and effectiveness of the gas early warning model proposed in this paper are verified. Realizing the classification of early warning of gas risks has important practical significance for improving the safety of coal mines.Yuxin HuangJingdao FanZhenguo YanShugang LiYanping WangMDPI AGarticleapriori algorithmassociation rules<i>k</i>-means algorithmoutlier detectiongas risks warningTechnologyTENEnergies, Vol 14, Iss 6889, p 6889 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	apriori algorithm association rules <i>k</i>-means algorithm outlier detection gas risks warning Technology T
spellingShingle	apriori algorithm association rules <i>k</i>-means algorithm outlier detection gas risks warning Technology T Yuxin Huang Jingdao Fan Zhenguo Yan Shugang Li Yanping Wang Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining
description	In the process of gas prediction and early warning, outliers in the data series are often discarded. There is also a likelihood of missing key information in the analysis process. To this end, this paper proposes an early warning model of coal face gas multifactor coupling relationship analysis. The model contains the <i>k</i>-means algorithm based on initial cluster center optimization and an Apriori algorithm based on weight optimization. Optimizing the initial cluster center of all data is achieved using the cluster center of the preorder data subset, so as to optimize the <i>k</i>-means algorithm. The optimized algorithm is used to filter out the outliers in the collected data set to obtain the data set of outliers. Then, the Apriori algorithm is optimized so that it can identify more important information that appears less frequently in the events. It is also used to mine and analyze the association rules of abnormal values and obtain interesting association rule events among the gas outliers in different dimensions. Finally, four warning levels of gas risk are set according to different confidence intervals, the truth and reliable warning results are obtained. By mining association rules between abnormal data in different dimensions, the validity and effectiveness of the gas early warning model proposed in this paper are verified. Realizing the classification of early warning of gas risks has important practical significance for improving the safety of coal mines.
format	article
author	Yuxin Huang Jingdao Fan Zhenguo Yan Shugang Li Yanping Wang
author_facet	Yuxin Huang Jingdao Fan Zhenguo Yan Shugang Li Yanping Wang
author_sort	Yuxin Huang
title	Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining
title_short	Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining
title_full	Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining
title_fullStr	Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining
title_full_unstemmed	Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining
title_sort	research on early warning for gas risks at a working face based on association rule mining
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/06881430ed074278856e993e62007a8c
work_keys_str_mv	AT yuxinhuang researchonearlywarningforgasrisksataworkingfacebasedonassociationrulemining AT jingdaofan researchonearlywarningforgasrisksataworkingfacebasedonassociationrulemining AT zhenguoyan researchonearlywarningforgasrisksataworkingfacebasedonassociationrulemining AT shugangli researchonearlywarningforgasrisksataworkingfacebasedonassociationrulemining AT yanpingwang researchonearlywarningforgasrisksataworkingfacebasedonassociationrulemining
_version_	1718434066734252032

Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining

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