%A HUANG Meng-ting, ZHANG Ling, JIANG Wen-chao %T Multi-type Relational Data Co-clustering Approach Based on Manifold Regularization %0 Journal Article %D 2019 %J Computer Science %R 10.11896/j.issn.1002-137X.2019.06.008 %P 64-68 %V 46 %N 6 %U {https://www.jsjkx.com/CN/abstract/article_18210.shtml} %8 %X With the development of big data applications,the size of multi-type relational data sampled from nonlinear manifolds is getting larger.The data geometric structure is more complicated,and the heterogeneous relational data are becoming extremely sparse.As a result,data mining becomes more difficult and less accurate.In order to solve this problem,this paper proposed a manifold nonnegative matrix tri-factorization(MNMTF) approach for multi-type relational data co-clustering.First of all,the correlation matrix is constructed with the natural relationship or content relevance of smaller-scale entities and it is decomposed into indicating matrix.The indicating matrix is used as the input of nonnegative matrix tri-factorization.Then,the manifold regularization is added on the basis of fast nonnegative matrix tri-factorization(FNMTF) to simultaneously cluster data inter-type relationships and intra-type relationships,improving the accuracy of clustering.Experiments show that the accuracy and performance of MNMTF algorithm are superior to the traditional co-clustering algorithms based on nonnegative matrix factorization.