Comprehensive Collection of Clustering Algorithms with MATLAB Implementation

This article presents MATLAB implementations of virtually all mainstream clustering algorithms, complete with runnable demo examples. The collection includes but is not limited to: hierarchical clustering, k-means clustering, expectation-maximization clustering, DBSCAN clustering, spectral clustering, and Gaussian mixture model clustering. These algorithms are implemented using core MATLAB functions like pdist and linkage for hierarchical clustering, kmeans for centroid-based methods, and custom functions for density-based approaches such as DBSCAN. The implementations feature parameter optimization techniques and data preprocessing routines to handle various dataset types. These clustering methods find applications across multiple domains including image processing (via pixel feature extraction), natural language processing (text vector clustering), and bioinformatics (gene expression pattern analysis). Each algorithm section includes detailed explanations of underlying principles, such as distance metric calculations for hierarchical clustering and expectation-maximization iterations for GMM. The code incorporates clear commenting describing key steps like cluster centroid initialization, convergence criteria checking, and cluster validation indices. This structure enables users to not only understand algorithmic workflows but also modify parameters and adapt the code for specific datasets and application scenarios through adjustable input arguments and modular function design.