关键词: 迁移学习, 类中心约束, 可能性C均值算法

Abstract: Compared with Fuzzy C-Means（FCM）, Possibilistic C-Means clustering algorithm（PCM） can deal with the data with noise and exception point better, but when dealing with the data set with strong viscosity, the clustering center of PCM algorithm will lead to the direct failure of clustering algorithm. To solve the above issue, this paper devises central-constraints and transfer based on source domain criterions, and applies these to PCM. It proposes Central-Constraints Possibilistic C-Means algorithms based on the Source Domain（CCSD_PCM for short）, which can achieve better clustering effect. Improved algorithm can use the cross-domain knowledge to support the clustering, so as to guarantee the clustering performance of the algorithm. Through the simulation data sets and real data sets, it verifies the above-mentioned advantages of the algorithm.

Key words: transfer learning, central-constraints, possibilistic C-means algorithms