A Proposal for Supervised Clustering with Dirichlet Process Using Labels. Peralta, B., Caro, A., & Soto, A. Pattern Recognition Letters, 80:52-57, 2016.
A Proposal for Supervised Clustering with Dirichlet Process Using Labels [link]Paper  abstract   bibtex   
Supervised clustering is an emerging area of machine learning, where the goal is to find class-uniform clusters. However, typical state-of-the-art algorithms use a fixed number of clusters. In this work, we propose a variation of a non-parametric Bayesian modeling for supervised clustering. Our approach consists of modeling the clusters as a mixture of Gaussians with the constraint of encouraging clusters of points with the same label. In order to estimate the number of clusters, we assume a-priori a countably infinite number of clusters using a variation of Dirichlet Process model over the prior distribution. In our experiments, we show that our technique typically outperforms the results of other clustering techniques.
@Article{	  peralta:etal:2016,
  author	= {B. Peralta and A. Caro and A. Soto},
  title		= {A Proposal for Supervised Clustering with Dirichlet
		  Process Using Labels},
  journal	= {Pattern Recognition Letters},
  volume	= {80},
  pages		= {52-57},
  year		= {2016},
  abstract	= {Supervised clustering is an emerging area of machine
		  learning, where the goal is to find class-uniform clusters.
		  However, typical state-of-the-art algorithms use a fixed
		  number of clusters. In this work, we propose a variation of
		  a non-parametric Bayesian modeling for supervised
		  clustering. Our approach consists of modeling the clusters
		  as a mixture of Gaussians with the constraint of
		  encouraging clusters of points with the same label. In
		  order to estimate the number of clusters, we assume
		  a-priori a countably infinite number of clusters using a
		  variation of Dirichlet Process model over the prior
		  distribution. In our experiments, we show that our
		  technique typically outperforms the results of other
		  clustering techniques.},
  url		= {http://www.sciencedirect.com/science/article/pii/S0167865516300976}
}
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