Aiming at shortcomings of the K-means algorithm to be improved， such as selection of initial center points and the problems that abnormal points and outliers can easily affect the clustering results， this paper proposes an improved K-means algorithm based on Tukey rules and optimizing initial center points selection. The proposed algorithm uses Tukey rules to construct core and non-core subsets， and divides the clustering process into two stages. At the same time， the strategy of increasing the center points one by one is implemented on the core subset to optimize the initial center points. The clustering results on 20 real-world datasets from UCI show that the proposed algorithm is better than the most popular K-means++ clustering algorithm and effectively improves the clustering performance.