Fuzzy Smooth Equilibrium Method for Clustering

Abstract

Clustering model plays an indispensable role in exploring data structures. To extend supervised learning to unsupervised, the maximum margin clustering model has been proposed. Maximum margin-based frameworks develop a powerful tool for supervised learning. It could yield good results by combining with some fuzzy clustering models. However, such methods characterized by high computational cost and are sensitive to the nearest neighbor relationships between data objects. Sometimes, they could lead to degenerate solutions. By reconstructing the Laplacian matrix with different similarity measurements, a new fuzzy smooth equilibrium clustering (FSEC) model is proposed. This model combines MMC with spectral clustering, but there is no need to solve the eigenvalue decomposition problem. Using the equilibrium regularization term can avoid degenerate solutions. Numerous experiments have established the effectiveness of the newly FSEC model.