  We have proposed a model based upon flocking on a complex network, and then
developed two clustering algorithms on the basis of it. In the algorithms,
firstly a \textit{k}-nearest neighbor (knn) graph as a weighted and directed
graph is produced among all data points in a dataset each of which is regarded
as an agent who can move in space, and then a time-varying complex network is
created by adding long-range links for each data point. Furthermore, each data
point is not only acted by its \textit{k} nearest neighbors but also \textit{r}
long-range neighbors through fields established in space by them together, so
it will take a step along the direction of the vector sum of all fields. It is
more important that these long-range links provides some hidden information for
each data point when it moves and at the same time accelerate its speed
converging to a center. As they move in space according to the proposed model,
data points that belong to the same class are located at a same position
gradually, whereas those that belong to different classes are away from one
another. Consequently, the experimental results have demonstrated that data
points in datasets are clustered reasonably and efficiently, and the rates of
convergence of clustering algorithms are fast enough. Moreover, the comparison
with other algorithms also provides an indication of the effectiveness of the
proposed approach.
