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Analysis and data mining of intellectual property using GRNN and SVM

  • Liying LiEmail author
original Article

Abstract

With the rapid development of cloud computing and big data technology and the increasing amount of data derived from intellectual property, data mining analysis methods to a certain extent have been already unable to meet the demand for data processing, thus affecting the development of cloud computing and big data. Therefore, this paper introduces two kinds of mathematical algorithms, GRNN and SVM, to analyze the intellectual property data. By comparing the two kinds of algorithms of data mining for intellectual property, we initially established analysis method of data mining technology based on intellectual property rights. By comparing the accuracy of the two algorithms, it is found that the classification accuracy of the target algorithm is higher than that of the comparison algorithm. Therefore, in the early stage, the sampling probability based on the GRNN network algorithm is better than SVM, but it is not high in the later stage. The SVM algorithm at the early stages of the data mining reduces the individual classifier’s classification accuracy. However, due to the gradual increase of the number of classifiers including the individual, the classification accuracy of the integrated classifier is continuously improved, and it makes the calculation of the sampling probability of the data more accurate. Therefore, it is concluded that the hybrid GRNN and SVM algorithms for intellectual property data mining is much better than the ordinary single algorithm, and it will be widely used in the future data mining data processing. It has laid the foundation for further research on the depth of intellectual property data processing in the future.

Keywords

Data mining GRNN SVM Intelligent analysis 

Notes

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Science and TechnologyNorth China Electric Power UniversityBaodingChina

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