Mean Shift Clustering
A fast implementation of mean-shift clustering using dual-tree range search. Given a dataset, this uses the mean shift algorithm to produce and return a clustering of the data.
Available Predicates
meanShift/12
Perform mean shift clustering on the data, returning a list of cluster assignments and centroids.
%% part of the predicate definition
meanShift( +float32, +integer,
+pointer(float_array), +integer, +integer,
-pointer(float_array), -integer,
-pointer(float_array), -integer, -integer,
+integer, +integer).Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| radius | +float | If distance of two centroids is less than it, one will be removed. If this value isn't positive, an estimation will be given when clustering. | 0.0 |
| maxIterations | +integer | Maximum number of iterations allowed before giving up iterations will terminate. | 1000 |
| data | +matrix | Dataset to cluster. | - |
| assignments | -vector | Vector to store cluster assignments in. | - |
| centroids | -matrix | Matrix in which centroids are stored. | - |
| foreceConvergence | +integer(bool) | Flag whether to force each centroid seed to converge regardless of maxIterations. | (1)true |
| useSeeds | +integer(bool) | Set true to use seeds. | (1)true |
Connected Links/Resources
If you want a more detailed explanation, then go to the python documentation. There is most of the time a good explanation on how the methods work and what the parameters do.
added some of the links from the python documentation