MATLAB Code Implementation for Spectral Clustering

We have successfully implemented a novel spectral clustering algorithm and developed a robust spectral clustering toolbox. The toolbox employs key MATLAB functions such as eig() for eigenvalue decomposition and kmeans() for final clustering, providing users with efficient spectral clustering analysis capabilities. It offers multiple functionalities including similarity matrix computation using Gaussian kernel functions, automated selection of optimal cluster numbers via eigenvalue gap analysis, and customizable parameter tuning for affinity matrices. Our optimized algorithm handles large-scale datasets effectively through sparse matrix operations and includes parallel computing implementations for enhanced performance. The toolbox enables flexible spectral clustering applications with demonstrated excellence in both accuracy and computational efficiency, allowing researchers to easily apply these methods to solve various practical problems and expand their research capabilities.