Spectral Clustering Method for Cluster Analysis with MATLAB Implementation

This paper presents a spectral clustering method for cluster analysis (MATLAB version) that can efficiently and rapidly partition multi-dimensional sample data. Spectral clustering is a graph theory-based clustering algorithm that operates by constructing a similarity matrix, performing spectral decomposition (eigenvalue/eigenvector analysis), and ultimately partitioning sample data into distinct clusters. This method has wide applications in data mining and pattern recognition fields, helping researchers better understand and analyze complex datasets. The MATLAB implementation typically involves key functions like pdist2 for distance calculation, affinity matrix generation using Gaussian kernel functions, and svd or eigs for eigenvalue decomposition. In practical applications, the MATLAB version of spectral clustering enhances clustering efficiency and accuracy through optimized matrix operations and built-in linear algebra routines, providing researchers and engineers with additional analytical tools and methodologies for handling high-dimensional data.