Data clustering using controlled consensus in complex networks

• We present a new graph-based clustering method.
• A new dissimilarity measurement via pinned dynamical systems is proposed.
• The system's convergence is assured under some parameter constraints.
• The method is effective, including for clusters with different sizes and shapes.

Recently, many network-based methods have been developed and successfully applied to cluster data. Once the underlying network has been constructed, a clustering method can be applied over its vertices and edges. In this paper, the concept of pinning control in complex networks is applied to cluster data. Firstly, an adaptive method for constructing sparse and connected networks is proposed. Secondly, a dissimilarity measure is computed via a dynamic system in which vertices are expected to reach a consensus state regarding a reference trajectory. The reference is forced into the system by pinning control. A theoretical analysis was carried out to prove the convergence of the dynamic system under certain parameter constraints. The results using real data sets have showed that the proposed method performs well in the presence of clusters with different sizes and shapes comparing to some well-known clustering methods.