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Full and Mini-batch Clustering of News Articles with Star-EM

  • Matthias Gallé
  • Jean-Michel Renders
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7224)

Abstract

We present a new threshold-based clustering algorithm for news articles. The algorithm consists of two phases: in the first, a local optimum of a score function that captures the quality of a clustering is found with an Expectation-Maximization approach. In the second phase, the algorithm reduces the number of clusters and, in particular, is able to build non-spherical–shaped clusters. We also give a mini-batch version which allows an efficient dynamic processing of data points as they arrive in groups. Our experiments on the TDT5 benchmark collection show the superiority of both versions of this algorithm compared to other state-of-the-art alternatives.

Keywords

Score Function News Article Bregman Divergence Large Spatial Database Clear Order 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Matthias Gallé
    • 1
  • Jean-Michel Renders
    • 1
  1. 1.Xerox Research Centre EuropeFrance

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