Title :
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
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;
Conference_Titel :
Intelligent Systems Design and Engineering Applications, 2013 Fourth International Conference on
Conference_Location :
Zhangjiajie
Print_ISBN :
978-1-4799-2791-3
DOI :
10.1109/ISDEA.2013.431