A Novel K-means Clustering Based on the Immune Programming Algorithm

This paper proposes a novel K-means clustering based on the immune programming algorithm after analyzing the advantages and disadvantages of the classical K-means clustering algorithm. The theory analysis and experimental results show that the algorithm not only avoids the local optima and is robust to initialization, but also increases the convergence speed.