Spectral Clustering

Spectral clustering is a method that groups data points based on their relationships, using concepts from mathematics called eigenvalues and eigenvectors. It constructs a similarity map, highlighting how points relate to each other, and then transforms this map into a form that makes natural groups more apparent. By analyzing the structure of this transformed data, spectral clustering efficiently identifies clusters that may be shaped irregularly, overcoming limitations of simpler methods. It’s especially useful when clusters are complex or intertwined, leveraging mathematical tools to reveal the underlying organization of data.