Gaussian mixture model (GMM) is a well-known model-based approach for data clustering. However, when the data samples are insufficient, the classical GMM-based clustering algorithms are not effective anymore. Referring to the idea of transfer clustering methods, this paper proposes a general transfer GMM-based clustering framework, which employs the important knowledge extracted from some known source domain to guide and improve the clustering on the target domain with small-scale data. Specifically, three traditional GMM-based clustering approaches are extended to the corresponding transfer clustering versions. Furthermore, to avoid the negative transfer problem, maximum mean discrepancy (MMD) is introduced to search the most matched source domain to provide more positive guidance for data clustering on the target domain. Experiments on synthetic and real-world datasets demonstrate the efficiency of the presented framework compared with several existing transfer clustering algorithms.

高斯混合模型（GMM）是一种众所周知的基于模型的数据聚类方法。但是，当数据样本不足时，传统的基于GMM的聚类算法将不再有效。参照转移聚类方法的思想，提出了一种基于转移GMM的通用聚类框架，该框架利用从一些已知源域中提取的重要知识来指导和改进小规模数据在目标域上的聚类。具体地说，将三种基于GMM的传统聚类方法扩展到了相应的传输聚类版本。此外，为避免负传输问题，引入了最大平均差异（MMD）来搜索最匹配的源域，从而为目标域上的数据聚类提供更积极的指导。