A Proposal for Supervised Clustering with Dirichlet Process Using Labels. Peralta, B., Caro, A., & Soto, A. *Pattern Recognition Letters*, 80:52-57, 2016. 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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