The input is a field of sampled states as points in phase space. The problem is to group the states into trajectories and then trajectories into trajectory bundles that share similar limit behaviors. Next, classify forms equivalence classes of points from the MST by deleting inconsistent edges in the MST that are significantly longer than their neighbor edges. Finally, neighboring trajectories are bundled using an equivalence relation comparing limit behaviors of the curves. This structure, the basis for Spatial Aggregation, applies across a variety of domains.
Different applications require different neighborhood relations.
For example, in an image analysis program, a point's neighbors could be the four or eight adjacent points. In the trajectory interpretation application, an object's neighbors could be its neighbors in a minimal spanning tree or Delaunay triangulation. Finally, a neighborhood relation could relate an object to its k nearest neighbors. The next main step, classification , forms equivalence classes of neighboring spatial objects. An object is considered equivalent to one of its neighbors if they are similar enough according to a user-specified equivalence predicate.