An efficient decentralized clustering algorithm for aggregation of noisy multi-mean data

We describe VarClust, a gossip-based decentralized clustering algorithm designed to support multi-mean decentralized aggregation in energy-constrained wireless sensor networks. We empirically demonstrate that VarClust is at least as accurate as, and requires less node-to-node communication (and hence consumes less energy) than, a state-of-the-art aggregation approach, affinity propagation. This superiority holds for both the clustering and aggregation phases of inference, and is demonstrated over a range of noise levels and for a range of random and small-world graph topologies.