Data clustering using side information dependent Chinese restaurant processes. Issue 2 (May 2016)

• Record Type: Journal Article
• Authors: Li, Cheng; Rana, Santu; Phung, Dinh; Venkatesh, Svetha
• Is Part Of: Knowledge and information systems. Volume 47:Issue 2(2016:May)
• Page Start: 463
• Page End: 488
• Publication Date: 2016-05
• Subjects: Side information -- Similarity -- Data clustering -- Bayesian nonparametric models; Expert systems (Computer science) -- Periodicals; Information storage and retrieval systems -- Periodicals; 006.33
• DOI: 10.1007/s10115-015-0834-7
• Languages: English
• ISSNs: 0219-1377

Abstract

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.