Clustering is a fundamental problem for a great variety fields such as pattern recognition, computer vision. A popular technique for clustering is based on K-means. However, it suffers from the four main disadvantages. Firstly, it is slow and scales poorly on the time. Secondly, it is often impractical to expect a user to specify the number of clusters. Thirdly, it may find worse local optima. Lastly, its performance heavily depends on the initial clustering centers. To overcome the above four disadvantages simultaneously, an effectively adaptive K-means clustering algorithm (AKM) is proposed in this paper. The AKM estimates the correct number of clusters and obtains the initial centers by the segmentation of the norm histogram in the linear normed space consisting of the data set, and then performs the local improvement heuristic algorithm for K-means clustering in order to avoid the local optima. Moreover, the kd-tree is used to store the data set for improving the speed. The AKM was tested on the synthetic data sets and the real images. The experimental results demonstrate the AKM outperforms the existing methods.