MATLAB Source Code for Pattern Recognition and Clustering Algorithms

This MATLAB-written source code implements pattern recognition and clustering functionality, incorporating most commonly used clustering methods. Key algorithms implemented include K-means clustering for centroid-based partitioning, hierarchical clustering with dendrogram visualization capabilities, and DBSCAN for density-based spatial clustering. These methods enable effective data classification and grouping through distinct computational approaches: K-means minimizes within-cluster variances, hierarchical clustering builds nested clusters via linkage methods, while DBSCAN identifies arbitrary-shaped clusters based on density connectivity.

The source files provide customizable parameter settings (such as number of clusters for K-means, linkage criteria for hierarchical clustering, and epsilon/minimum points for DBSCAN) and algorithmic optimization options including distance metric selection (Euclidean, Manhattan, cosine) and convergence criteria adjustments. The implementation features modular functions for easy integration into different applications, with error handling for invalid inputs and performance monitoring for large datasets. This toolkit offers robust and flexible solutions for pattern recognition tasks, enabling comprehensive data structure analysis and feature extraction through well-documented MATLAB functions with example usage scenarios.