Abstract: Due to the influence of initial class centers K-Means algorithm is easy to fall into local optimal clustering result. K-Means based on Genetic Algorithm and particle swarm optimization algorithm could improve the performance of the basic K-Means in a certain extent, however, Genetic Algorithm and particle swarm optimization algorithm themselves are easy to fall into local optimal solution too. Thus, in the paper, a clustering method based on K-means clustering algorithm and cuckoo search algorithm is presented, which is able to overcome the drawbacks of basic K-means clustering algorithm. Further, the proposed approach has been compared with K-means clustering algorithm optimized by genetic algorithm and particle swarm optimization algorithm. The experimental results show that the method can effectively improve the shortcoming of k-means algorithm, which is a robust clustering method and has better global search capability in comparison with K-means clustering algorithm based on genetic algorithm and particle swarm optimization algorithm.