Dirichlet process mixture models with pairwise constraints for data clustering Li, Cheng, Rana, Santu, Phung, Dinh and Venkatesh, Svetha 2016, Dirichlet process mixture models with pairwise constraints for data clustering, Annals of data science, vol. 3, no. 2, pp. 205-223, doi: 10.1007/s40745-016-0082-z. Attached Files Name Description MIMEType Size Downloads Title Dirichlet process mixture models with pairwise constraints for data clustering Author(s) Li, Cheng Rana, Santu orcid.org/0000-0003-2247-850X Phung, Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Journal name Annals of data science Volume number 3 Issue number 2 Start page 205 End page 223 Total pages 19 Publisher Springer Place of publication Berlin, Germany Publication date 2016 ISSN 2198-5804 2198-5812 Keyword(s) Bayesian nonparametric Dirichlet process Mixture models Pairwise constraints Constrained clustering Short-text clustering Summary The Dirichlet process mixture (DPM) model, a typical Bayesian nonparametric model, can infer the number of clusters automatically, and thus performing priority in data clustering. This paper investigates the influence of pairwise constraints in the DPM model. The pairwise constraint, known as two types: must-link (ML) and cannot-link (CL) constraints, indicates the relationship between two data points. We have proposed two relevant models which incorporate pairwise constraints: the constrained DPM (C-DPM) and the constrained DPM with selected constraints (SC-DPM). In C-DPM, the concept of chunklet is introduced. ML constraints are compiled into chunklets and CL constraints exist between chunklets. We derive the Gibbs sampling of the C-DPM based on chunklets. We further propose a principled approach to select the most useful constraints, which will be incorporated into the SC-DPM. We evaluate the proposed models based on three real datasets: 20 Newsgroups dataset, NUS-WIDE image dataset and Facebook comments datasets we collected by ourselves. Our SC-DPM performs priority in data clustering. In addition, our SC-DPM can be potentially used for short-text clustering. Language eng DOI 10.1007/s40745-016-0082-z Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2016, Springer Persistent URL http://hdl.handle.net/10536/DRO/DU:30083829 Document type: Journal Article Collection: Centre for Pattern Recognition and Data Analytics Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 31 May 2016, 17:00:10 EST Tue, 31 May 2016, 17:00:40 EST Wed, 01 Jun 2016, 05:01:23 EST Wed, 01 Jun 2016, 06:08:30 EST Thu, 25 Aug 2016, 14:12:32 EST Fri, 02 Dec 2016, 05:02:29 EST Thu, 08 Dec 2016, 09:04:12 EST Wed, 21 Dec 2016, 08:30:05 EST Fri, 16 Jun 2017, 12:20:54 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 141 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Tue, 31 May 2016, 17:00:40 EST

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