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
[1]ROWEIS S T,SAUL L K.Nonlinear dimensionality reduction by locally linear embedding[J].Science,2000,290(5500):2323-2326.
[2]BELKIN M,NIYOGI P.Laplacian eigenmaps for dimensionality reduction and data representation [J].Neural Computation,2003,15(6):1373-1396.
[3]AILEM M,ROLE F,NADIF M.Co-clustering document-term matrices by direct maximization of graph modularity[C]∥ACM International on Conference on Information and Knowledge Management.New York:ACM Press,2015:1807-1810.
[4]HONDA K,TANAKA D,NOTSU A.Incremental algorithms for fuzzy co-clustering of very large cooccurrence matrix[C]∥IEEE International Conference on Fuzzy Systems.Piscataway:IEEE Press,2014:2494-2499.
[5]LEE D D,SEUNG H S.Learning the parts of objects with nonnegative matrix factorization[J].Nature,1999,401(21):788-791.
[6]LEE D D,SEUNG H S.Algorithms for non-negative matrix factorization[C]∥Neural Information Processing Systems.New York:NIPC Press 2000:535-541.
[7]DING C,HE X,SIMON H D,et al.On the equivalence of nonnegative matrix factorization and spectral clustering[C]∥SIAM International Conference on Data Mining.Philadelphia:SIAM Press,2005:606-610.
[8]DING C,LI T,PENG W,et al.Orthogonal nonnegative matrix tri-factorizations for clustering[C]∥ACM SIGKDD Internatio-nal Conference on Knowledge Discovery and Data Mining.New York:ACM Press,2006:126-135.
[9]LI Z,WU X.Weighted nonnegative matrix tri-factorization for co-clustering[C]∥IEEE International Conference on TOOLS with Artificial Intelligence.Piscataway:IEEE Press,2011:811-816.
[10]BUONO N D,PIO G.Non-negative Matrix Tri-Factorization for co-clustering:An analysis of the block matrix[J].Information Sciences,2015,301(20):13-26.
[11]GU Q,ZHOU J.Co-clustering on manifolds[C]∥ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM Press,2009:359-368.
[12]WANG S,HUANG A.Penalized nonnegative matrix tri-factorization for co-clustering[J].Expert Systems with Applications,2017,78(C):64-73.
[13]WANG S,GUO W.Robust co-clustering via dual local learning and high-order matrix factorization[J].Knowledge-Based Systems,2017,138(15):176-187.
[14]WANG H,NIE F,HUANG H,et al.Fast nonnegative matrix tri-factorization for large-scale data co-clustering[C]∥International Joint Conference on Artificial Intelligence.Menlo Park:AAAI Press,2011:1553-1558.
[15]SHEN G,YANG W,WANG W,et al.Large-scale heteroge-neous data co-clustering based on nonnegative matrix factorization[J].Journal of Computer Research and Development,2016,53(2):459-466.(in Chinese)
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