| 摘要 |
A method for clustering large datasets in which a number N of data instances with a number n fields is linearly weighted to an n-dimensional mesh with (for example) m grid points per dimension, a number of "intelligent agents" is placed randomly on the mesh. These agents move along the grid according to special rules that cause them to find grid points that have the largest weight. All clusters can be determined in this fashion and the clusters can be ranked in "strength", these maxima are then used as the "centroid" of each cluster. If desired, the mesh can be gridded finer around these "centroids" to obtain finer scaling, and all data points within a certain specified distance of these centroids are considered to form a cluster.
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