  This paper introduces a model based upon games on an evolving network, and
develops three clustering algorithms according to it. In the clustering
algorithms, data points for clustering are regarded as players who can make
decisions in games. On the network describing relationships among data points,
an edge-removing-and-rewiring (ERR) function is employed to explore in a
neighborhood of a data point, which removes edges connecting to neighbors with
small payoffs, and creates new edges to neighbors with larger payoffs. As such,
the connections among data points vary over time. During the evolution of
network, some strategies are spread in the network. As a consequence, clusters
are formed automatically, in which data points with the same evolutionarily
stable strategy are collected as a cluster, so the number of evolutionarily
stable strategies indicates the number of clusters. Moreover, the experimental
results have demonstrated that data points in datasets are clustered reasonably
and efficiently, and the comparison with other algorithms also provides an
indication of the effectiveness of the proposed algorithms.
