Analysis of Coal Mine Hidden Danger Correlation Based on Improved A Priori Algorithm

Author

Liu Shuangyue ; Peng Li

Author_Institution

Sch. of Civil & Environ. Eng., Univ. of Sci. & Technol., Beijing, China

fYear

2013

fDate

6-7 Nov. 2013

Firstpage

112

Lastpage

116

Abstract

This paper outlines an improved Apriori algorithm that suits coal mine hidden danger data. Then preprocess the data according to the characteristics of safety hidden danger data of coal mine workplaces, including data import and extraction. Put this algorithm embedded in a coal mine hidden danger investigation system. After this is coal mine hidden danger data association rules mining based on the improved Apriori algorithm to achieve getting valuable association rules inside the coal mine hidden dangers. Provide recommendations for improvement for enterprises in the hidden danger management and prevention and ultimately there is an important practical significance in preventing accidents and reducing the loss of the accidents.

Keywords

accident prevention; coal; data mining; industrial accidents; mining; occupational safety; production engineering computing; accident prevention; coal mine hidden danger correlation; coal mine workplaces; data association rules mining; hidden danger management; hidden danger prevention; improved Apriori algorithm; Algorithm design and analysis; Association rules; Coal mining; Databases; Face; Safety; FM; LabVIEW; Virtual Instrument;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Design and Engineering Applications, 2013 Fourth International Conference on

Conference_Location

Zhangjiajie

Print_ISBN

978-1-4799-2791-3

Type

conf

DOI

10.1109/ISDEA.2013.431

Filename

6843408