吉林大学学报(工学版) ›› 2011, Vol. 41 ›› Issue (4): 1077-1082.

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Integrating K-means and non-negative matrix factorization to ensemble document clustering

XU Sen1,2, LU Zhi-mao2, GU Guo-chang2   

  1. 1.School of Information Engineering, Yancheng Institute of Technology,Yancheng 224051,China;2.Pattern Recognition and Natural Computation Laboratory, Harbin Engineering University, Harbin 150001, China
  • Received:2009-07-18 Online:2011-07-01 Published:2011-07-01

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Abstract:

Non-negative Matrix Factorization (NMF) is brought forth into document cluster ensemble problem. In order to get a stable result, K-means algorithm using minimum and maximum principle is performed. Then the result of K-means is provided to NMF algorithm as the initial factor matrix. NMF is further performed on adjacent matrix of hypergraph to attain the basis matrix and efficient matrix. The final clustering result is obtained according to the coefficient matrix. Whereupon, a NMFK algorithm is proposed and tested on real-world document sets. Results show that NMFK outperforms other cluster ensemble techniques and is very efficient.

Key words: computer application, clustering analysis, document cluster ensemble, non-negative matrix factorization, K-means

CLC Number: 

  • TP391
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