Data clustering using side information dependent Chinese restaurant processes

Cheng Li, Santu Rana, Dinh Phung, Svetha Venkatesh

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)463-488
Number of pages26
JournalKnowledge and Information Systems
Volume47
Issue number2
DOIs
Publication statusPublished - May 2016
Externally publishedYes

Keywords

  • Bayesian nonparametric models
  • Data clustering
  • Side information
  • Similarity

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