基于 T-DPC 优化算法的电子商务财务管理分析与应用研究

Yilan Wang, Yao Shan
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引用次数: 0

摘要

鉴于电子商务企业的财务数据错综复杂、涉及多个方面,本文提出了一种用于分析这些企业财务管理的 T-DPC 算法。该算法利用 t-SNE 方法降低了财务数据的维度,同时还基于 K 近邻概念实现了增强型 DPC 算法,以分析财务数据集群。结果表明,在 PID 和 Wine 数据集上测试后,经 t-SNE 优化的 DPC 算法的 F-measure 指标比 DPC 算法提高了 16.7% 和 3.07%,在 Aggregation、D31 和 R15 数据集上的运行时间比 DPC 算法快 16.2。因此,该算法对电子商务企业财务分析具有参考意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis and Application Research of E-Commerce Financial Management Based on T-DPC Optimization Algorithm
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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