Weijia Lu1,2, Zhuangzhi Yan1. 1. School of Communication and Information Engineering, Shanghai University, Shanghai, China. 2. Informatic Department, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.
Abstract
BACKGROUND: Bronchitis is considered a non-specific inflammation in the peripheral tissues of the trachea and bronchus. Many therapeutic schemes for chronic bronchitis have been reported in existing research. METHODS: This work attempted to conduct optimization analysis of the therapeutic scheme for chronic bronchitis using a data mining method. To overcome the shortfalls of the current fuzzy C-means clustering (FCM) algorithm, this research proposed an improved kernel fuzzy C-means (KFCM) clustering algorithm. The improved KFCM algorithm solved traditional cluster algorithm problems in two ways: firstly FCM clustering was mapped in high-dimensional kernel space; and the samples in the initial input space R(S) were mapped to high-dimensional feature space R(p). Finally, the improved and traditional algorithms by computer simulation experiments. RESULTS: Based on the analysis of the simulation experiments on IRIS dataset in this research, improved KFCM algorithm could improve calculation accuracy by 10% because the initial value greatly decreased the number of iterations and improved the accuracy of the calculation. CONCLUSION: The improved KFCM algorithm was used to optimize the relationship between data structures in the process of iteration clustering so as to accelerate iteration convergence. The simulation results show that the improved KFCM algorithm performs better in terms of both calculating performance and clustering correctness.
BACKGROUND:Bronchitis is considered a non-specific inflammation in the peripheral tissues of the trachea and bronchus. Many therapeutic schemes for chronic bronchitis have been reported in existing research. METHODS: This work attempted to conduct optimization analysis of the therapeutic scheme for chronic bronchitis using a data mining method. To overcome the shortfalls of the current fuzzy C-means clustering (FCM) algorithm, this research proposed an improved kernel fuzzy C-means (KFCM) clustering algorithm. The improved KFCM algorithm solved traditional cluster algorithm problems in two ways: firstly FCM clustering was mapped in high-dimensional kernel space; and the samples in the initial input space R(S) were mapped to high-dimensional feature space R(p). Finally, the improved and traditional algorithms by computer simulation experiments. RESULTS: Based on the analysis of the simulation experiments on IRIS dataset in this research, improved KFCM algorithm could improve calculation accuracy by 10% because the initial value greatly decreased the number of iterations and improved the accuracy of the calculation. CONCLUSION: The improved KFCM algorithm was used to optimize the relationship between data structures in the process of iteration clustering so as to accelerate iteration convergence. The simulation results show that the improved KFCM algorithm performs better in terms of both calculating performance and clustering correctness.
Entities:
Keywords:
Data mining; KFCM algorithm; chronic bronchitis; medical information