Survey of spectral clustering based on graph theory

Ling Ding, Chao Li, Di Jin, Shifei Ding

Abstract: Highlights•This paper introduces the basic concept of graph theory, reviews the properties of Laplacian matrix and the traditional graph cuts method. Starting from four aspects in the realization process of spectral clustering (construction of similarity matrix, establishment of Laplacian matrix, selection of eigenvectors, and determination of the number of clusters), we have summarized in detail some representative algorithms in recent years.•Some successful applications of spectral clustering are summarized. In each aspect, the shortcomings of spectral clustering and some representative improved algorithms are emphatically analyzed.•This paper comprehensively analyzes some research on spectral clustering that has not yet been in-depth, and gives prospects on some valuable research directions.