Support Vector Clustering Machine

This project provides the primary MATLAB program and selected subroutines for implementing Support Vector Clustering (SVC). SVC is a machine learning algorithm that groups data points into distinct clusters by finding optimal separating boundaries in high-dimensional feature space. The main program coordinates the core SVC workflow, handling data preprocessing, kernel function computation (typically using Gaussian RBF kernel), and cluster boundary identification through support vector optimization. Key subroutines include functions for kernel matrix calculation, quadratic programming optimization for support vector selection, and cluster labeling based on adjacency matrices. The implementation demonstrates how SVC leverages support vectors to define cluster boundaries without requiring pre-specified cluster numbers, making it particularly valuable for exploratory data analysis. Through this MATLAB implementation, researchers can efficiently study SVC's application in pattern recognition and data segmentation tasks. The code structure facilitates modification of kernel parameters and optimization settings, allowing users to adapt the algorithm for specific datasets. This program serves as a valuable resource for those investigating support vector clustering methodologies and their practical implementations in data science applications.