Abstract: In order to analyze the clustering results of high-order heterogeneous data at the overlaps of different clusters more efficiently, a fuzzy co-clustering algorithm was developed for high-order heterogeneous data (HFCC). HFCC algorithm minimized distances between objects and centers of clusters in each feature space. The update rules for fuzzy memberships of objects and weights of features were derived, and then an iterative algorithm was designed for the clustering process. Additionally, convergence of iterative algorithm was proved. In order to estimate the number of clusters, GXB validity index was proposed by generalizing the XB validity index, which could measure the quality of high-order clustering results. Finally, experimental results show that HFCC can efficiently mine the overlapped clusters and the qualities of clustering results of HFCC are superior five classical hard high-order co-clustering algorithms. Additionally, GXB validity index can efficiently estimate the number of high-order clusters.