ISODATA Clustering Algorithm Implementation

I have independently developed an ISODATA clustering program that has been thoroughly debugged and validated in MATLAB. This implementation performs cluster analysis on datasets, automatically grouping data points into distinct categories based on their characteristic features. The algorithm incorporates key ISODATA features including dynamic threshold control for cluster merging and splitting, automatic adjustment of cluster numbers based on predefined parameters, and centroid recalculation through iterative optimization. Cluster analysis serves as a fundamental data analysis technique that helps reveal underlying relationships and patterns within datasets. My MATLAB implementation includes core functions for distance calculation using Euclidean metrics, centroid initialization through k-means++ optimization, and automatic termination criteria based on convergence thresholds. The program effectively handles variance-based cluster validation and implements minimum distance classification for point assignment. By utilizing this ISODATA clustering program, researchers can gain deeper insights into their datasets, enabling more accurate data-driven conclusions. The code structure includes modular functions for data preprocessing, iterative centroid updates, and cluster validity checks, making it suitable for various numerical dataset applications.