An Improved K-Means Algorithm Based on Initial Clustering Center Optimization

The K-means algorithm is widely known for its simplicity and fastness in text clustering. However, the selection of the initial clus-tering center with the traditional K-means algorithm is some random, and therefore, the fluctuations and instability of the cluster-ing results are strongly affected by the initial clustering center. This paper proposed an algorithm to select the initial clustering center to eliminate the uncertainty of central point selection. The experiment results show that the improved K-means clustering algorithm is superior to the traditional algorithm.