K-means Optimal Clustering Number Determination Method Based on Clustering Center Optimization

The traditional K-means clustering algorithm requires a certain number of clustering, usually the value of clustering number is assumed by priori rules. Besides, the choice of K-means initial clustering centers is also randomized, which made the clustering result instability. In view of the above factors, improved the selection of initial centers of K-means clusters, and a new method to determine the optimal clustering number is proposed based on that. The experimental results show that the proposed method can get better clustering results and have higher accuracy, better stability and better astringency.