An Improved K-means Algorithm Based on Adaptive PSO and Its Application in Cluster Analysis of Electronic Medical Records
MU Yanzhou
DING Weiping
GAO Feng
YU Liguo
ZHANG Qiong
Abstract：In view of the shortcomings of traditional K-means algorithm in over reliance on the initial cluster center selection, this paper presents a K-means clustering algorithm based on adaptive PSO. The algorithm designs an adaptive inertia weight func?tion to dynamically adjust the PSO,and then converge with the K-means algorithm to make the initial clustering center of K-means adaptively generate the global optimal. Finally,the improved clustering algorithm is applied to the clustering of medical electronic medical records. The experimental results show that the algorithm has higher accuracy and efficiency when it is applied in cluster analysis of electronic medical records.
Keywords：adaptive PSOinertia weightK-means algorithmdisease clustering
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 1861-1865 )