Analysis and Application Research of E-Commerce Financial Management Based on T-DPC Optimization Algorithm

Yilan Wang; Yao Shan

doi:10.31181/dmame722024943

Authors

Yilan Wang Accounting Department, North China Institute of Science and Technology, Langfang 065201, China https://orcid.org/0000-0003-2650-0575
Yao Shan NSchool of Emergency Technology & Management, North China Institute of Science and Technology, Langfang 065201, China https://orcid.org/0000-0003-3369-0282

DOI:

https://doi.org/10.31181/dmame722024943

Keywords:

Financial data, DPC, T-SNE, Prediction, Clustering

Abstract

Given the intricate, multifaceted nature of financial data in e-commerce enterprises, this article presents a T-DPC algorithm for analyzing financial management in these businesses. The algorithm utilizes the t-SNE method to reduce the dimensionality of financial data, whilst also implementing an enhanced DPC algorithm based on the K-nearest neighbor concept to analyze financial data clusters. The results show that the F-measure metrics of the DPC algorithm optimized by t-SNE improve 16.7% and 3.07% over the DPC algorithm after testing on the PID and Wine datasets, and its running time is faster than the DPC algorithm on the Aggregation, D31, and R15 datasets by 16.2. Therefore, the algorithm has reference significance for the financial analysis of e-commerce enterprises.

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References

Mai, K., Saeed, W., & Medi, I. (2022). Churn prediction in digital game-based learning using data mining techniques: logistic regression, decision tree, and random forest. Applied Soft Computing, 118(1), 108491. https://doi.org/10.1016/j.asoc.2022.108491

Munshi, M., Shrimali, T., & Gaur, S. (2022). A review of enhancing online learning using graph-based data mining techniques. Soft Computing, 26(12):5539-5552. https://doi.org/10.1007/s00500-022-07034-7

Safarnejad, L., Xu, Q., Ge, Y., & Chen, S. (2021). A multiple feature category data mining and machine learning approach to characterize and detect health misinformation on social media. IEEE Internet Computing, 25(5), 43-51. https://doi.org/10.1109/MIC.2021.3063257

Pistikou, V., Tsanana, E., & Poufinas, T. (2021). A Financial Analysis Approach on the Impact of Economic Interdependence on Interstate Conflicts. Theoretical Economics Letters, 11(5),947-961. https://doi.org/10.4236/tel.2021.115060

Cohen, S., Manes-Rossi, F., Brusca, I., & Caperchione, E. (2021). Guest editorialHappy endings and successful stories in public sector financial management: a lesson drawing perspective. International Journal of Public Sector Management, 34(4), 393-406. https://doi.org/10.1108/IJPSM-05-2021-347

Qiu, W. (2021). Enterprise financial risk management platform based on 5G mobile communication and embedded system. Microprocessors and Microsystems, 80, 103594. https://doi.org/10.1016/j.micpro.2020.103594

Xiong, L. (2021, April). Enterprise financial shared service platform based on big Data. In Journal of Physics: Conference Series (Vol. 1852, No. 3, p. 032004). IOP Publishing. https://doi.org/10.1088/1742-6596/1852/3/032004

Yu, L. Q., & Guo, F. X. (2020). Research on Cost Management Optimization of Financial Sharing Center Based on RPA. Procedia Computer Science, 166, 115-119. https://doi.org/10.1016/j.procs.2020.02.031

Mashrur, A., Luo, W., Zaidi, N. A., & Robles-Kelly, A. (2020). Machine learning for financial risk management: a survey. IEEE Access, 8, 203203-203223. https://doi.org/10.1109/ACCESS.2020.3036322

Yang, R., Yu, L., Zhao, Y., Yu, H., Xu, G., Wu, Y., & Liu, Z. (2020). Big data analytics for financial Market volatility forecast based on support vector machine. International Journal of Information Management, 50, 452-462. https://doi.org/10.1016/j.ijinfomgt.2019.05.027

Nunkoo, H. B. S., Gonpot, P. N., Sookia, N. U. H., & Ramanathan, T. V. (2022). Autoregressive conditional duration models for high frequency financial data: an empirical study on mid cap exchange traded funds. Studies in Economics and Finance, 39(1), 150-173. https://doi.org/10.1108/SEF-04-2021-0146

Cai, J., Wei, H., Yang, H., & Zhao, X. (2020). A novel clustering algorithm based on DPC and PSO. IEEE Access, 8, 88200-88214. https://doi.org/10.1109/ACCESS.2020.2992903

Long, Z., Gao, Y., Meng, H., Yao, Y., & Li, T. (2022). Clustering based on local density peaks and graph cut. Information Sciences, 600, 263-286. https://doi.org/10.1016/j.ins.2022.03.091

Yu, H., Chen, L., & Yao, J. (2021). A three-way density peak clustering method based on evidence theory. Knowledge-Based Systems, 211, 106532. https://doi.org/10.1016/j.knosys.2020.106532

Qi, Y., Ren, J., Sun, N., & Yu, Y. (2021, July). Application of clustering algorithm by data mining in the analysis of smart grid from the perspective of electric power. In Journal of Physics: Conference Series (Vol. 1982, No. 1, p. 012018). IOP Publishing. https://doi.org/10.1088/1742-6596/1982/1/012018

Ye, J., Zou, J., Gao, J., Zhang, G., Kong, M., Pei, Z., & Cui, K. (2021). A new frequency hopping signal detection of civil UAV based on improved k-means clustering algorithm. IEEE Access, 9, 53190-53204. https://doi.org/10.1109/ACCESS.2021.3070491

Brunet-Saumard, C., Genetay, E., & Saumard, A. (2022). K-bMOM: A robust Lloyd-type clustering algorithm based on bootstrap median-of-means. Computational Statistics & Data Analysis, 167, 107370. https://doi.org/10.1016/j.csda.2021.107370

Kobak, D., & Linderman, G. C. (2021). Initialization is critical for preserving global data structure in both t-SNE and UMAP. Nature biotechnology, 39(2), 156-157. https://doi.org/10.1038/s41587-020-00809-z

Bajal, E., Katara, V., Bhatia, M., & Hooda, M. (2022). A review of clustering algorithms: comparison of DBSCAN and K-mean with oversampling and t-SNE. Recent Patents on Engineering, 16(2), 17-31. https://doi.org/10.2174/1872212115666210208222231

Gove, R., Cadalzo, L., Leiby, N., Singer, J. M., & Zaitzeff, A. (2022). New guidance for using t-SNE: Alternative defaults, hyperparameter selection automation, and comparative evaluation. Visual Informatics, 6(2), 87-97. https://doi.org/10.1016/j.visinf.2022.04.003

Almomany, A., Ayyad, W. R., & Jarrah, A. (2022). Optimized implementation of an improved KNN classification algorithm using Intel FPGA platform: Covid-19 case study. Journal of King Saud University-Computer and Information Sciences, 34(6), 3815-3827. https://doi.org/10.1016/j.jksuci.2022.04.006

Trieu, N. M., & Thinh, N. T. (2022). A study of combining knn and ann for classifying dragon fruits automatically. Journal of Image and Graphics, 10(1), 28-35. https://doi.org/10.18178/joig.10.1.28-35