Clustering K-means++ algorithm process

This flowchart illustrates the K-means++ algorithm process, a refined version of the traditional K-means clustering technique. It begins by randomly selecting a sample point as the initial cluster center and proceeds to determine whether the algorithm has converged or reached the maximum number of iterations. If not, it checks if the required number of K cluster centers has been established. Each sample is then assigned to the nearest cluster based on calculated distances. This systematic approach ensures efficient clustering by minimizing the initial selection bias, ultimately leading to improved accuracy and performance in data analysis.