Computer Science ›› 2019, Vol. 46 ›› Issue (6): 64-68.doi: 10.11896/j.issn.1002-137X.2019.06.008

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Multi-type Relational Data Co-clustering Approach Based on Manifold Regularization

HUANG Meng-ting, ZHANG Ling, JIANG Wen-chao   

  1. (School of Computers,Guangdong University of Technology,Guangzhou 510006,China)
  • Received:2018-05-06 Published:2019-06-24

Abstract: 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.

Key words: Multi-type relational data, Manifold regularization, Nonnegative matrix factorization, Correlation matrix

CLC Number: 

  • TP391
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