ISODATA Clustering Algorithm Demonstration Program

The ISODATA clustering algorithm demonstration program is implemented precisely following Algorithm 3 from Section 2.4.4 "Dynamic Clustering Method" in Sun Jixiang's "Modern Pattern Recognition". The implementation uses Euclidean distance as the primary distance metric for measuring similarity between data points. The core algorithm operates by iteratively grouping samples into clusters based on their similarity characteristics, enabling better data understanding and analysis. The iterative process involves calculating cluster centroids, reassigning samples based on minimum distance criteria, and dynamically adjusting cluster structures through merging and splitting operations. The algorithm continues this cycle until meeting predefined stopping conditions such as maximum iterations or cluster stability thresholds. Key implementation features include dynamic cluster management that adapts to data distribution changes, automated centroid recalculation after each iteration, and configurable parameters for controlling cluster separation and consolidation. The program incorporates validation checks for minimum cluster size requirements and optimal cluster count determination. By employing the ISODATA clustering algorithm, users can effectively organize and interpret complex datasets, providing substantial support for subsequent data analysis tasks and decision-making processes. The implementation includes visualization components to demonstrate cluster evolution throughout the iteration process.