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As the most typical graph clustering method, spectral clustering is popularand attractive due to the remarkable performance, easy implementation, andstrong adaptability. Classical spectral clustering measures the edge weights ofgraph using pairwise Euclidean-based metric, and solves the optimal graphpartition by relaxing the constraints of indicator matrix and performingLaplacian decomposition. However, Euclidean-based similarity might cause skewgraph cuts when handling non-spherical data distributions, and the relaxationstrategy introduces information loss. Meanwhile, spectral clustering requiresspecifying the number of clusters, which is hard to determine without enoughprior knowledge. In this work, we leverage the path-based similarity to enhanceintra-cluster associations, and propose MeanCut as the objective function andgreedily optimize it in degree descending order for a nondestructive graphpartition. This algorithm enables the identification of arbitrary shapedclusters and is robust to noise. To reduce the computational complexity ofsimilarity calculation, we transform optimal path search into generating themaximum spanning tree (MST), and develop a fast MST (FastMST) algorithm tofurther improve its time-efficiency. Moreover, we define a density gradientfactor (DGF) for separating the weakly connected clusters. The validity of ouralgorithm is demonstrated by testifying on real-world benchmarks andapplication of face recognition. The source code of MeanCut is available athttps://github.com/ZPGuiGroupWhu/MeanCut-Clustering.

MeanCut: A Greedy-Optimized Graph Clustering via Path-based Similarity and Degree Descent Criterion