Phased array antennas, with high directivity and low sidelobe levels, are extensively employed in radar and communication systems. As the array scale increases, the complexity and cost of controlling each element individually are increased significantly. To overcome this challenge and improve the efficiency, one of the effective engineering solutions is subarray partitioning. Traditional clustering algorithms, such as K-means, require a predefined number of subarrays, and limits the flexibility, especially in scenarios with complex geometries or irregular distributions. A subarray partitioning approach is presented in this paper using the Iterative Self-Organizing Data Analysis (ISODATA) algorithm, and the proposed scheme dynamically adjusts the number of subarrays based on the geometric characteristics of the array. According to numerical experiments results, good performance of sidelobe suppression, beamforming and partitioning flexibility have been achieved, and shows better characteristic compared with the K-means. This algorithm has potential application in large-scale phased arrays.