基于金融大数据的管理平台的设计和分析
Yuhua Chen1,2, Hasri Mustafa2, Xuandong Zhang3
1Zhongyuan Institute of Science and Technology, Zhengzhou, China.
PeerJ. Computer science
|June 22, 2023
概括
新的模糊集群和局部异常因素算法改善了财务数据分类和异常检测. 这提高了企业在Hadoop平台上的财务数据处理效率和准确性.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 金融技术 金融技术
背景情况:
- 传统的财务会计面临着新技术和市场发展的局限性.
- 目前的财务数据分类系统存在较高的错误率,阻碍了业务价值创造.
- 改善金融大数据的利用对于提高财务管理信息的应用至关重要.
研究的目的:
- 开发使用模糊集群的自动化财务数据分类系统.
- 使用基于邻近关系的局部异常因子 (NLOF) 算法实现异常数据检测.
- 设计一个金融数据管理平台,将这些算法集成到分布式Hadoop架构上.
主要方法:
- 用于自动化财务数据分类的模糊集群算法.
- 局部异常因子 (LOF) 算法与邻近关系 (NLOF) 进行异常检测.
- 分布式Hadoop架构与MapReduce框架用于算法的并行处理.
主要成果:
- 拟议的平台显示了改进的算法性能和财务数据分类的准确性.
- 通过MapReduce并行执行模糊集群和LOF算法可以提高处理效率.
- 实验结果显示,与现有方法相比,运行效率和分类准确性更高.
结论:
- 开发的财务数据管理平台有效地解决了传统会计的局限性.
- 在Hadoop上整合模糊集群和LOF算法显著改善了财务数据处理.
- 该平台为企业财务数据管理和分析提供了卓越和有效的解决方案.
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