An improved hierarchical K-means clustering algorithm was developed to automatically determine the number of clusters without needing to guess beforehand. By using a hierarchical structure of space, the algorithm first performs an initial K-means clustering and then decides if further clustering is needed at a finer level. This process is repeated to create a hierarchical clustering tree, resulting in better clustering results compared to traditional K-means clustering methods. Simulation results on UCI datasets showed the effectiveness of this approach.