MeanShift Properties 
The MeanShift type exposes the following members.
Name  Description  

Bandwidth 
Gets or sets the bandwidth (radius, or smoothness)
parameter to be used in the meanshift algorithm.
 
Clusters 
Gets the clusters found by Mean Shift.
 
ComputeLabels 
Gets or sets whether cluster labels should be computed
at the end of the learning iteration. Setting to False
might save a few computations in case they are not necessary.
 
ComputeProportions 
Gets or sets whether cluster proportions should be computed
at the end of the learning iteration. Setting to False
might save a few computations in case they are not necessary.
 
Dimension 
Gets the dimension of the samples being
modeled by this clustering algorithm.
 
Distance 
Gets or sets the IMetricT used to
compute distances between points in the clustering.
 
Maximum 
Gets or sets the maximum number of neighbors which should be
used to determine the direction of the meanshift during the
computations. Default is zero (unlimited number of neighbors).
 
MaxIterations 
Gets or sets the maximum number of iterations to
be performed by the method. If set to zero, no
iteration limit will be imposed. Default is 0.
 
ParallelOptions 
Gets or sets the parallelization options for this algorithm.
(Inherited from ParallelLearningBase.)  
Token 
Gets or sets a cancellation token that can be used
to cancel the algorithm while it is running.
(Inherited from ParallelLearningBase.)  
Tolerance 
Gets or sets the relative convergence threshold
for stopping the algorithm. Default is 1e5.
 
UseAgglomeration 
Gets or sets whether to use the agglomeration shortcut,
meaning the algorithm will stop early when it detects that
a sample is going to follow the same path as another sample
when running in parallel.
 
UseParallelProcessing  Obsolete.
Gets or sets whether the algorithm can use parallel
processing to speedup computations. Enabling parallel
processing can, however, result in different results
at each run.
 
UseSeeding 
Gets or sets whether to use seeding to initialize the algorithm.
With seeding, new points will be sampled from an uniform grid in
the range of the input points to be used as seeds. Otherwise, the
input points themselves will be used as the initial centroids for
the algorithm.
