基于最大平均差异的迁移模糊C均值聚类

In this paper, a Transfer Fuzzy C-Means clustering algorithm based on Maximum Mean Discrepancy (TFCM-MMD) is proposed. TFCM-MMD solves the problem that the transfer learning effect of the transfer fuzzy C-means clustering algorithm is weakened when the data distribution between source domain and target domain is very different. The algorithm measures inter-domain differences based on the maximum mean discrepancy criterion, and reduces the differences of data distribution between source domain and target domain in the common subspace by learning the projection matrix of source domain and target domain, so as to improve the effect of transfer learning. Finally, experiments based on synthetic datasets and medical image segmentation datasets verify further the effectiveness of TFCM-MMD algorithm in solving transfer clustering problems with large inter-domain differences.