MATLAB Source Code for K-Means Dynamic Clustering Algorithm in Pattern Recognition

This document provides MATLAB source code for implementing the dynamic clustering process using the k-means algorithm in pattern recognition. Dynamic clustering involves iteratively grouping similar data points based on their feature characteristics through an automated process. The k-means algorithm represents a fundamental unsupervised machine learning approach that partitions data points into k clusters based on similarity metrics. The implementation includes core algorithmic components such as centroid initialization, distance calculation (typically Euclidean distance), cluster assignment updates, and centroid repositioning through iterative optimization. The provided MATLAB code features functions for handling dynamic data clustering scenarios, including: - Data preprocessing and normalization routines - Iterative centroid optimization with convergence checking - Cluster validation and performance evaluation metrics - Visualization tools for cluster analysis results This implementation supports various applications including image segmentation, data mining operations, and pattern classification tasks. The algorithm significantly improves clustering accuracy through optimized centroid positioning and enhances computational efficiency via vectorized operations, enabling effective analysis of large-scale datasets in a structured and interpretable manner. The code architecture allows for customization of distance metrics, convergence thresholds, and cluster validation methods to adapt to specific pattern recognition requirements.