Ensemble clustering via fusing global and local structure information

Jiaxuan Xu, Taiyong Li, Duzhong Zhang, Jiang Wu

Abstract: Highlights•A novel framework that fuses samples’ global and local structures is proposed for ensemble clustering.•Two ensemble clustering models, FSEC-C and FSEC-Z are derived using the framework.•L2,1<math><msub is="true"><mrow is="true"></mrow><mrow is="true"><mn is="true">2</mn><mo is="true">,</mo><mn is="true">1</mn></mrow></msub></math>-norm regularization is posed to increase the robustness of the CA self-enhancement structure.•Comparison with state-of-the-art ensemble clustering approaches verifies the advantages of the proposed models.