Dirichlet Process Mixture Models with Pairwise Constraints for Data Clustering

Cheng Li (), Santu Rana, Dinh Phung and Svetha Venkatesh
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Cheng Li: Deakin University
Santu Rana: Deakin University
Dinh Phung: Deakin University
Svetha Venkatesh: Deakin University

Annals of Data Science, 2016, vol. 3, issue 2, No 5, 205-223

Abstract: Abstract 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.

Keywords: Bayesian nonparametric; Dirichlet process; Mixture models; Pairwise constraints; Constrained clustering; Short-text clustering (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s40745-016-0082-z

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