ISODATA Clustering Algorithm with Annotated Implementation

The ISODATA clustering algorithm is a widely-used methodology for cluster analysis of datasets. This implementation includes comprehensive code annotations to facilitate understanding and practical application. The demonstration utilizes the renowned Iris dataset to showcase ISODATA's clustering performance through actual code execution. The algorithm incorporates iterative centroid updates with dynamic cluster management features, including automatic merging of similar clusters and splitting of heterogeneous clusters based on standard deviation thresholds. Through dataset analysis and clustering operations, we can effectively categorize and interpret complex data structures. The annotated code provides detailed explanations of algorithmic phases: initialization parameters, cluster assignment logic, merge/split conditions, and termination criteria. These technical annotations enable readers to master both the theoretical foundations and practical implementation of this adaptive clustering technique.