Nonnegative Matrix Factorization for Document Clustering: A Survey

  • Ehsan Hosseini-Asl
  • Jacek M. Zurada
Conference paper

DOI: 10.1007/978-3-319-07176-3_63

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8468)
Cite this paper as:
Hosseini-Asl E., Zurada J.M. (2014) Nonnegative Matrix Factorization for Document Clustering: A Survey. In: Rutkowski L., Korytkowski M., Scherer R., Tadeusiewicz R., Zadeh L.A., Zurada J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science, vol 8468. Springer, Cham

Abstract

Nonnegative Matrix Factorization (NMF) is a popular dimension reduction technique of clustering by extracting latent features from high-dimensional data and is widely used for text mining. Several optimization algorithms have been developed for NMF with different cost functions. In this paper we apply several methods of NMF that have been developed for data analysis. These methods vary in using different cost function for matrix factorization and different optimization algorithms for minimizing the cost function. Reuters Document Corpus is used for evaluating the performance of each method. The methods are compared with respect to their accuracy, entropy, purity and computational complexity and residual mean square root error. The most efficient methods in terms of each performance measure are also recognized.

Keywords

Nonnegative Matrix Factorization Document clustering optimization algorithm 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ehsan Hosseini-Asl
    • 1
  • Jacek M. Zurada
    • 1
    • 2
  1. 1.Electrical and Computer Engineering DepartmentUniversity of LouisvilleLouisvilleUSA
  2. 2.Information Technology InstituteAcademy of ManagementLodzPoland

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