【摘要】 目前多数聚类方法主要关注单视图数据，对于多视图聚类的研究相对不足，而现有的多视图聚类方法往往侧重于视图间的信息学习，忽略视图内信息的充分挖掘.对此，文中提出基于自适应结构增强的对比协同多视图属性图聚类(Contrastive Collaborative Multi-view Attribute Graph Clustering Based on Adaptive Structure Enhancement, ACCMVC).首先，设计自适应结构增强策略，结合节点重要性和节点特征复杂关系生成边权重，用于生成视图的新邻接矩阵，进而生成结构增强图.然后，将边权重引入邻域对比学习，对视图及其结构增强图使用视图内加强邻域对比学习，在多个视图间使用视图间加强邻域对比学习.最后，考虑到多视图中视图的重要性存在差别，引入注意力机制，计算每个视图的权重并进行融合.在数据集上的实验表明，ACCMVC的聚类性能较优.更多还原

【Abstract】 Most clustering methods mainly focus on single-view data, while the research on multiview clustering remains relatively under-explored. Existing multi-view clustering methods often emphasize learning inter-view information while neglecting the thorough exploitation of intra-view information. In this paper, a contrastive collaborative multi-view attribute graph clustering based on adaptive structure enhancement(ACCMVC) is proposed. First, an adaptive structure enhancement strategy is designed to generate edge weights by combining node importance and the complex relationships among node features.These edge weights are applied to construct new adjacency matrices for the views, and thereby structureenhanced graphs are generated. Second, edge weights are introduced into neighborhood contrastive learning. Intra-view enhanced neighborhood contrastive learning is applied to views and their structureenhanced graphs, while inter-view enhanced neighborhood contrastive learning is utilized among multiple views. Finally, considering the varying view importance, an attention mechanism is introduced to calculate the weight of each view for effective fusion. Experiments on multiple datasets demonstrate that ACCMVC achieves superior clustering performance.更多还原