Dynamic Clustering and Iterative Self-Organizing Data Analysis Algorithm (ISODATA) Implementation in MATLAB

Implementing Dynamic Clustering or Iterative Self-Organizing Data Analysis Algorithm (ISODATA) using MATLAB enables effective clustering analysis of 2D data. Dynamic Clustering is an algorithm capable of automatically adjusting cluster centers and determining optimal cluster numbers, adapting to data variations and uncertainties through iterative optimization. ISODATA, as a sample-based clustering algorithm, employs iterative refinement processes including cluster merging and splitting operations to continuously enhance clustering accuracy and stability. The MATLAB implementation typically involves key functions such as kmeans initialization, centroid calculation using mean functions, and distance computation with pdist or custom Euclidean distance formulas. Through MATLAB's implementation, researchers can better understand and analyze clustering characteristics in 2D data, providing valuable insights for subsequent data processing and decision-making workflows. The algorithm's adaptive nature makes it particularly suitable for datasets where the optimal number of clusters is unknown beforehand.