MATLAB Implementation of ISODATA Algorithm with Code Description

The ISODATA algorithm is a classic unsupervised clustering algorithm that offers a key advantage over K-means by dynamically adjusting the number of clusters during execution. The MATLAB implementation of this algorithm involves several critical stages with specific code considerations. First, preprocessing of the Iris dataset is required. The Iris dataset, a benchmark classification dataset, contains 150 samples with 4 feature dimensions each. Before algorithm execution, data normalization is typically performed using MATLAB functions like normalize() or zscore() to ensure equal importance across all feature dimensions. The initialization phase requires setting multiple key parameters through code configuration. These include expected cluster count, merge and split thresholds, minimum sample constraints, and maximum iteration limits. These parameters significantly impact algorithm convergence and final clustering performance. Unlike K-means with fixed cluster numbers, ISODATA dynamically adjusts cluster counts based on sample distribution patterns. The iterative process forms the algorithm core. Each iteration involves three main steps implemented through MATLAB programming: 1. Sample assignment: Calculating Euclidean distances between each data point and cluster centers using pdist2() function, followed by classification based on nearest-neighbor principles 2. Cluster updating: Recomputing cluster centroids using mean() or median() functions 3. Splitting and merging: The distinctive feature where ISODATA evaluates whether to split overly dispersed clusters (based on standard deviation thresholds) or merge closely positioned clusters (using distance metrics), implemented through conditional statements and centroid manipulation Termination conditions typically involve maximum iteration counts or cluster center movement thresholds. When meeting termination criteria, the algorithm outputs final clustering results and cluster quantities. Application on the Iris dataset demonstrates how the algorithm partitions 150 samples into appropriate categories, with comparison against original classifications providing performance evaluation. The MATLAB implementation effectively demonstrates dynamic clustering advantages, particularly suitable for scenarios where the optimal cluster count cannot be predetermined. Through parameter adjustments, users can explore various clustering schemes, offering significant value in pattern recognition and data mining applications. Key MATLAB functions involved include kmeans() for initial clustering, cluster quality evaluation metrics, and visualization tools like scatter3() for multidimensional data representation.