The k-means algorithm is the most popular cluster algorithm. but for big dataset clustering with many clusters. it will take a lot of time to find all the clusters. This paper proposed a new acceleration method based on the thought of dynamical and immediate adjustment of the center K-means with triangle inequality. The triangle inequality is used to avoid redundant distance computations; But unlike Elkan’s algorithm. the centers are divided into outer-centers and inner-centers for each data point in the first place. and only the tracks of the lower bounds to inner-centers are kept; On the other hand. by adjusting the data points cluster by cluster and updating the cluster center immediately right after finishing each cluster’s adjustment. the number of iteration is effectively reduced. The experiment results show that our algorithm runs much faster than Elkan’s algorithm with much less memory consumption when the cluster center number is larger than 20 and the dataset records number is greater than 10 million. and the speedup becomes better when the k increases.