Abstract: A major problem of learning vector quantization(LVQ) clustering algorithm is its sensitivity to the initialization,affecting the clustering precision．This paper fist introduced the theory of LVQ clustering algorithm,then used immune clonal algorithm to optimize the initial values of LVQ．And the paper used IRIS data to test this new method．Comparing the evolving LVQ with the standard LVQ,the experiment results indicate that the approach of LVQ based on immune clonal algorithm has obvious stability to initial weights．

Key words: LVQ clustering algorithm,Immune clonal algorithm,Optimization