Research on NMF Based Hierarchical Clustering Methods

Fang Li; Qun Xiong Zhu

doi:10.4028/www.scientific.net/KEM.439-440.1306

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HomeKey Engineering MaterialsKey Engineering Materials Vols. 439-440Research on NMF Based Hierarchical Clustering...

Research on NMF Based Hierarchical Clustering Methods

Abstract:

LSI based hierarchical agglomerative clustering algorithm is studied. Aiming to the problems of LSI based hierarchical agglomerative clustering method, NMF based hierarchical clustering method is proposed and analyzed. Two ways of implementing NMF based method are introduced. Finally the result of two groups of experiment based on the TanCorp document corpora show that the method proposed is effective.

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Key Engineering Materials (Volumes 439-440)

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1306-1311

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https://doi.org/10.4028/www.scientific.net/KEM.439-440.1306

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June 2010

Authors:

Fang Li, Qun Xiong Zhu

Keywords:

Hierarchical Clustering, Latent Semantic Indexing, Non-Negative Matrix Factorization, Term Document Matrix

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References

[1] A Pons -porrata, etc, Topic discovery based on txt mining techniques， Information processing & management (2007).

Google Scholar

[2] Zhao, Y., & Karypis, G. Empirical and theoretical comparisons of selected criterion functions for document clustering. Machine Learning, 55(3)(2004), pp.311-331.

DOI: 10.1023/b:mach.0000027785.44527.d6

Google Scholar

[3] Dupret, G. Latent concepts and the number orthogonal factors in latent semantic , In: Proceedings of the 26th annual international ACM SIGIR conference(2003), pp.221-226.

DOI: 10.1145/860435.860477

Google Scholar

[4] Dupret, G. Latent Semantic Indexing with a Variable Number of Orthogonal Factors, In: Proceedings of the RIAO 2004, Coupling approaches, coupling media(2004), pp.673-685.

Google Scholar

[5] Liu Weixiang, Zheng Nanning, You Qubo. Non-negative matrix factorization and its applications in pattern recognition. Chinese Science Bulletin. 51 (2006) , pp.17-18.

DOI: 10.1007/s11434-005-1109-6

Google Scholar

[6] A. Pascual-Montano, J. M. Carazo, K. Kochi, D. Lehmann, and R. D. Pascual-Marqui, Non-smooth Non-Negative Matrix Factorization (nsNMF), IEEE Transactions on Pattern Analysis and Machine Intelligence. 28 (2006) , pp.403-415.

DOI: 10.1109/tpami.2006.60

Google Scholar

[7] A. Pascual-Montano, P. Carmona-Saez, M. Chagoyen, F. Tirado, J. M. Carazo, and R. D. Pascual-Marqui, bioNMF: a versatile tool for non-negative matrix factorization in biology, BMC Bioinformatics. 7, 366 (2006).

DOI: 10.1186/1471-2105-7-366

Google Scholar

[8] Daniel D. Lee and H. Sebastian Seung. Learning the parts of objects by non-negative matrix factorization. Nature. 401 (6755) (1999), pp.788-791.

DOI: 10.1038/44565

Google Scholar

[9] Daniel D. Lee and H. Sebastian Seung . Algorithms for Non-negative Matrix Factorization. Advances in Neural Information Processing Systems 13. Cambridge, MA: MIT Press, 13: pp.556-562 (2001).

Google Scholar

[10] Chih-Jen Lin. Projected Gradient Methods for Non-negative Matrix Factorization. Neural Computation, 19 (10) ( 2007).

Google Scholar

[11] Ngoc-Diep Ho, Paul Van Dooren and Vincent Blondel . Descent Methods for Non-negative Matrix Factorization. Numerical Linear Algebra in Signals. (2007).

DOI: 10.1007/978-94-007-0602-6_13

Google Scholar

[12] Sonbo Tan, Yuefen Wang. Chinese text categorization corpora -TanCorp V1. 0. http: /www. searchforum. org. cn/tansongbo/corpus. htm.

Google Scholar

[13] Songbo Tan et al. A Novel Refinement Approach for Text Categorization. ACM CIKM( 2005).

Google Scholar