Improved K-means Algorithm Based on Threshold Value Radius
Jiaqi Song, Fei Li, Ruxiang Li · IOP Conference Series Earth and Environmental Science · 2020
Abstract K-means algorithm is a classical clustering algorithm, which needs to specify K value artificially and chooses the initial clustering center randomly. However, it is easy to fall into local optimal solution. In order to overcome the shortcomings of K-means algorithm, an improved algorithm based on threshold value radius is proposed in this paper. The initial clustering center and threshold radius are determined automatically by the average value of Euclidean distance between data. The experimental results show that the method proposed can effectively and accurately overcome the shortcomings of the traditional K-means algorithm.