Big Data Mining Method of Thermal Power Based on Spark and Optimization Guidance

Author(s):  
Mingcheng Song ◽  
Li Jia
2013 ◽  
Vol 859 ◽  
pp. 373-376
Author(s):  
Fei Song ◽  
Gang Cheng ◽  
Feng Rui Sun ◽  
Dong Liang Li

In this paper we give a brief introduction of the data mining method used in thermal power system optimization. The issues as well as the influence of energy material property (take coal as example) on performance matching and optimizing of power plant are overviewed. The development of this domain is presented to readers. Finally, we draw several conclusions. First, Data Mining could have a deeply and widely use in optimization of thermal power plant. Second, the energy material property has a deeply impact on the safe operation and economic operation. The investigation is of great necessary.


Author(s):  
Yafei Wang

Through big data mining, enterprises can deeply understand the consumer preferences, behavior characteristics, market demand and other derived data of customers, so as to provide the basis for formulating accurate marketing strategies. Therefore, this paper proposes a marketing management big date mining method based on deep trust network model. This method first preprocesses the big data of marketing management, including data cleaning, data integration, data transformation and data reduction, and then establishes a big data mining model by using deep trust network to realize the research on the classification of marketing management data. Experimental results show that the proposed method has 99.08% accuracy, the capture rate reaches 88.11%, and the harmonic average between the accuracy and the recall rate is 89.27%, allowing for accurate marketing strategies.


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