Haihui Xin
ABSTRACT
Cross-border e-commerce is undergoing in-depth integration and innovation within and across industries, and traditional e-commerce trading platforms have been incapable to meet the requirement of e-commerce development. Moreover, the construction of an innovation ecosystem is being stepped up. By integrating existing resources and strengthening information sharing and exchanges between enterprises, a new idea of jointly building an innovation ecosystem is put forward to creat a collaborative and integrated development model. Therefore, an improved IVAEGAN-based fuzzy clustering algorithm for incomplete data (IVAEGAN-FCM) is proposed based on the Internet of Things, which can better extract the hidden features and data distribution in the data. Besides, the Nash equilibrium of the generator and the discriminator are applied to make the generated data more accurate, and VAE is used as the GAN generator to construct a new generative model. In addition, in order to obtain more effective information, the incomplete data set is reconstructed to select the nearest neighbor sample set for the missing data according to the nearest neighbor rule, and the median value of its attribute is used as the missing sample label. The label variables are added to the IVAEGAN model training to improve the accuracy of the valuation.
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Index Terms
- Research on Data Integrity of E-commerce Platforms in the Internet of Things under Cross-border
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