Abstract
Online financial education resources are massive. Traditional algorithms are affected by the load of resource processing nodes, resulting in low accuracy and long mining time. Therefore, a massive online financial education resource mining algorithm based on Apriori TIDs is proposed. The Apriori TIDS algorithm is used to establish the characteristic equation of massive online financial education resources. By extracting the number of principal factors of the characteristic vector, the proportion of financial education resources in each dimension of each resource processing node in its total resources is calculated, so as to obtain the residual value of financial education resources of the resource processing node, and calculate the dynamic weight of financial education resources based on this, The residual load capacity of resource processing nodes is obtained. Combined with the design of massive online financial education resource mining algorithm, the mining of massive online financial education resources is realized. Experimental results show that the proposed algorithm can not only improve the accuracy of mining, but also shorten the mining time and have better mining performance.
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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Luo, Y. (2022). Mining Algorithm of Massive Online Financial Education Resources Based on Apriori TIDS. In: Fu, W., Sun, G. (eds) e-Learning, e-Education, and Online Training. eLEOT 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 454. Springer, Cham. https://doi.org/10.1007/978-3-031-21164-5_23
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DOI: https://doi.org/10.1007/978-3-031-21164-5_23
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