AFSC: An Improved Spectral Clustering Based on Adaptive Neighbor and Fuzzy Affiliation

Spectral clustering (SC) is a hot research topic in graph theory today, which is centered on constructing similarity matrix. Many previous works have studied how to construct a higher-quality similarity matrix. However, most of these works ignore the connection between constructing similarity matrix and the overall distribution of the datasets. This problem will lead to low quality and lack of robustness in the similarity matrix when the data volume and dimensionality are expanded, which leads to poor clustering performance. Therefore, to solve the above problems, an Adaptive Fuzzy Spectral Clustering (AFSC) model is proposed in this paper. The model considers the local neighbor and global fuzzy affiliation information of the samples to construct the objective function, which can obtain the similarity matrix that satisfies the overall distribution of the datasets. Meanwhile, we propose a weight definition method to balance the above two kinds of information, which can prevent the adverse effect of information loss on the quality of the similarity matrix. The similarity matrix obtained by the AFSC model will have high quality and strong robustness, which leads to excellent clustering performance. In experiments, we verified that the AFSC model can obtain excellent clustering performance and outperform some state-of-the-art clustering models as it is applied to several datasets of different sizes and dimensions.