Distributed C-means data clustering algorithm
Gabriele Oliva, Roberto Setola, Christoforos N. Hadjicostis · 2016
In this paper we provide a distributed and asynchronous implementation of the C-means data clustering algorithm to let the agents in a sensor network partition themselves based on the observations available at each node (e.g., sensor data, positions, etc.) and to identify a small set of values which are representative of the observations. The clusters thus obtained are not mutually exclusive, in that each node is allowed to belong with different intensity to the different clusters. The proposed approach amounts to repeated depth-first visits of the network and imposes low requirements on memory, communication bandwidth and algorithmic complexity.