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Process and Application of Data Mining in the University Library


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Abstract


Characteristics of the technical and economic data of mining enterprises are multi-dimensionality and nonlinearity. The sales price data of mineral products is an important economic indicator of mining enterprises, and the geological data is an important technical data. The analysis method of the technical and economic data is researched using technologies of big data analysis and data mining. The fluctuation pattern and influencing factors of the mineral products price are analyzed. The prediction model of the mineral products price is established using artificial neural network. The results show that the practicability of the prediction model is strong, and the prediction accuracy is high. During the process of mineral development, due to the limitation of technical conditions and equipment conditions, lots of geological data have been lost, which reduces the accuracy of the orebody shape and that of reserves estimation. Based on techniques of geostatistics and artificial neural network, the prediction model of the geological missing data is established. By using the model, the regularity of geological data of single borehole, the regularity of geological data of group boreholes and the regularity of geological data of all boreholes is discussed and analyzed. It has been proved that most of the geological missing data can be predicted and interpolated, and results of prediction and interpolation are reliable.

KeyWords
big data; university library; data mining



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