A feature-emphasized clustering method for 2D vector field

Large-scale vector data produce the vector field clustering in flow visualization. To emphasize essential flow features, a new clustering method for 2D vector fields is proposed in this paper. With this method, the vector field is firstly initialized as a cluster, which is then iteratively divided into a hierarchy of clusters. During the iteration, clusters are segmented with streamlines instead of straight lines. This change enables it to emphasize flow features, since streamlines are consistent with flow behaviors, and clusters shaped by streamlines are aligned to the underlying flow. It is easy to capture flow patterns and features from resulting clusters. Moreover, our method improves representative vectors of clusters, leading to a more efficient approximation to the original field. Test results show that it is superior to other similar methods in terms of preserving flow features and approximating vector fields.