系统管理学报2012，Vol.21Issue(4)：461-469,9.
基于粒子群的K均值算法和粗糙集理论的财务预警
Early Financial Warning Based on PSO K-means Clustering Algorithm and Rough Set Theory
鲍新中1
作者信息
- 1. 北京科技大学经济管理学院,北京100083
- 折叠
摘要
Abstract
For a listed company's financial crisis warning, we adopt K-means clustering algorithm based on Particle Swarm Optimization to overcome the influence of subjective classification. We then combine Rough Set theory to evaluate the listed company's financial status comprehensively. Using the financial data of 127 listed companies in home appliance manufacturing in 2008, we apply K-means clustering algorithm based on PSO to divide the sampled companies into four categories, and Rough Set theory to calculate each company's comprehensive financial status scores, and test the validity of the results. The results show that K-means clustering algorithm based on PSO can divide companies into reasonable categories, and 88. 2% of the results generated from the two methods coincides.关键词
粗糙集/粒子群/K均值聚类/财务预警Key words
particle swarm optimization/K-means clustering/rough set/early financial warning分类
管理科学引用本文复制引用
鲍新中..基于粒子群的K均值算法和粗糙集理论的财务预警[J].系统管理学报,2012,21(4):461-469,9.
