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Topic Number Estimation by Consensus Soft Clustering with NMF

  • Takeru Yokoi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6485)

Abstract

We propose here a novel method to estimate the number of topics in a document set using consensus clustering based on Non-negative Matrix Factorization (NMF). It is useful to automatically estimate the number of topics from a document set since various approaches to extract topics can determine their number through heuristics. Consensus clustering makes it possible to obtain a consensus of multiple results of clustering so that robust clustering is achieved and the number of clusters is regarded as the optimized number. In this paper, we have proposed a novel consensus soft clustering algorithm based on NMF and estimated an optimized number of topics by searching through a robust classification of documents for the topics obtained.

Keywords

Consensus Clustering Estimation of the number of topics Soft Clustering Topic extraction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Takeru Yokoi
    • 1
  1. 1.Tokyo Metropolitan College of Industrial TechnologyJapan

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