Affiliations: [a] School of Communication and Information Engineering, Shanghai University, Shanghai, China | [b] Informatic Department, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
Correspondence:
[*]
Corresponding author: Weijia Lu, School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China. Tel.: +86 13862908262; Fax: +86 051385583180; E-mail:[email protected]
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.
Keywords: Data mining, chronic bronchitis, KFCM algorithm, medical information
