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Knowledge and Information Systems

, Volume 47, Issue 2, pp 463–488 | Cite as

Data clustering using side information dependent Chinese restaurant processes

  • Cheng LiEmail author
  • Santu Rana
  • Dinh Phung
  • Svetha Venkatesh
Regular Paper

Abstract

Side information, or auxiliary information associated with documents or image content, provides hints for clustering. We propose a new model, side information dependent Chinese restaurant process, which exploits side information in a Bayesian nonparametric model to improve data clustering. We introduce side information into the framework of distance dependent Chinese restaurant process using a robust decay function to handle noisy side information. The threshold parameter of the decay function is updated automatically in the Gibbs sampling process. A fast inference algorithm is proposed. We evaluate our approach on four datasets: Cora, 20 Newsgroups, NUS-WIDE and one medical dataset. Types of side information explored in this paper include citations, authors, tags, keywords and auxiliary clinical information. The comparison with the state-of-the-art approaches based on standard performance measures (NMI, F1) clearly shows the superiority of our approach.

Keywords

Side information Similarity Data clustering Bayesian nonparametric models 

Notes

Acknowledgments

We thank anonymous reviewers for their very useful comments and suggestions.

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Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Cheng Li
    • 1
    Email author
  • Santu Rana
    • 1
  • Dinh Phung
    • 1
  • Svetha Venkatesh
    • 1
  1. 1.Centre for Pattern Recognition and Data AnalyticsDeakin UniversityGeelongAustralia

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