We consider the problem of decentralized clustering and estimation over
multi-task networks, where agents infer and track different models of interest.
The agents do not know beforehand which model is generating their own data.
They also do not know which agents in their neighborhood belong to the same
cluster. We propose a decentralized clustering algorithm aimed at identifying
and forming clusters of agents of similar objectives, and at guiding
cooperation to enhance the inference performance. One key feature of the
proposed technique is the integration of the learning and clustering tasks into
a single strategy. We analyze the performance of the procedure and show that
the error probabilities of types I and II decay exponentially to zero with the
step-size parameter. While links between agents following different objectives
are ignored in the clustering process, we nevertheless show how to exploit
these links to relay critical information across the network for enhanced
performance. Simulation results illustrate the performance of the proposed
method in comparison to other useful techniques
