Fuzzy Granular-Balls Based Spectral Clustering

In real-world scenarios, datasets characterized by nonlinear separability and fuzziness are quite common. Although fuzzy granular-balls generated by fuzzy c-means can capture the fuzziness of the datasets, it exhibits limitations in effectively handling nonlinearly separable datasets. Due to the advantage of spectral clustering in handling nonlinearly separable datasets, we fully leverage this capability to propose a novel clustering model, namely, fuzzy granular-balls based spectral clustering (FGBSC). It enhances spectral clustering by introducing fuzzy granular-balls as inputs, and effectively addresses the nonlinear separability of the datasets on the basis of capturing the fuzziness of the datasets. We perform experiments to evaluate the effectiveness of the proposed method in handling nonlinearly separable datasets.