传统多视图聚类方法假设每一个视图的数据都是完整的。然而，现实生活中有些数据在某些视图上缺失，由此产生了缺失多视图聚类问题。现有的缺失多视图聚类方法大多是基于核矩阵和非负矩阵分解提出的。这些方法大多只是学习一个共有的聚类结构，没有充分利用已有的数据信息推断缺失的数据。基于星型生成对抗网络 (Star-GAN) 和子空间学习，本文中提出了一种缺失多视图聚类算法 SSPMVC。SSPMVC 充分利用已有的数据信息，用基于 StarGAN 的生成模型生成视图缺失的数据，捕获了视图数据的完整性和一致性全局结构，然后将补全的多视图数据在子空间进行聚类。SSPMVC将生成模型和聚类模型联合训练，交替优化生成模型和聚类模型。实验结果表明，论文提出的算法优于与之比较的经典的多视图聚类方法。

The traditional multi-view clustering method assumes that the data of each view is complete． However，there is a lack of data in some views in real life，and thus leads to the problem of partial multi-view clustering．Most of the existing partial multi-view clustering methods are based on kernel matrix and non-negative matrix de-composition，and most of them are just learning a common clustering structure，rather than making full use of the existing data information to infer the missing data． Based on StarGAN and subspace learning，a partial multi-view clustering algorithm (SSPMVC) was proposed in this study． SSPMVC makes full use of the existing data informa-tion to generate the missing data with the generation model based on StarGAN，captures the integrity and consisten-cy global structure of the data，and then clusters the completed multi-view data in the subspace． The generation model and clustering model were trained jointly by SSPMVC，and the generation model and clustering model were alternately optimized． The experimental results show that the algorithm proposed in this paper is superior to the classical multi-view clustering method．