摘要
传统方法受噪声点影响,存在分类精准低的问题。为此,提出基于聚类算法的海量医院财务数据精准分类方法。在对大数据去噪原理基础上,通过PNCC模型对财务数据去噪处理,避免噪声对数据分类结果产生影响;采取自适应邻域选择方法降维处理去噪后财务数据,构建聚类算法中QS-KFCM模型,将预处理后医院财务数据输入QS-KFCM模型中,完成海量医院财务数据的精准分类。实验表明方法可有效提高财务数据分类精度。
The classification process is affected by noise points,exists low classification accuracy in the classification method.A method for accurately classifying massive hospital financial data based on clustering algorithm is proposed.Based on the principle of denoising big data,the financial data is denoised through the PNCC model to prevent noise from affecting the results of data classification.Then adopt the adaptive neighborhood selection method to reduce the dimension of the denoised financial data,construct the QS-KFCM model,and input the preprocessed hospital financial data into the QS-KFCM model to complete the accurate classification of the massive hospital financial data.The experimental show that the classification accuracy of the proposed method is high.
作者
朱建霞
ZHU Jian-xia(General Accountant's Office of Nantong Fourth People's Hospital,Nantong 226005 China)
出处
《自动化技术与应用》
2023年第4期79-82,共4页
Techniques of Automation and Applications
关键词
聚类算法
数据降维
数据去噪
QS-KFCM模型
小样本容量阈值
clustering algorithm
data dimensionality reduction
data denoising
QS-KFCM model
small sample size threshold
