  In this paper, we propose a model-based clustering method (TVClust) that
robustly incorporates noisy side information as soft-constraints and aims to
seek a consensus between side information and the observed data. Our method is
based on a nonparametric Bayesian hierarchical model that combines the
probabilistic model for the data instance and the one for the side-information.
An efficient Gibbs sampling algorithm is proposed for posterior inference.
Using the small-variance asymptotics of our probabilistic model, we then derive
a new deterministic clustering algorithm (RDP-means). It can be viewed as an
extension of K-means that allows for the inclusion of side information and has
the additional property that the number of clusters does not need to be
specified a priori. Empirical studies have been carried out to compare our work
with many constrained clustering algorithms from the literature on both a
variety of data sets and under a variety of conditions such as using noisy side
information and erroneous k values. The results of our experiments show strong
results for our probabilistic and deterministic approaches under these
conditions when compared to other algorithms in the literature.
