The MeanShift type exposes the following members.
Gets or sets the bandwidth (radius, or smoothness) parameter to be used in the mean-shift algorithm.
Gets the clusters found by Mean Shift.
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.
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.
Gets the dimension of the samples being modeled by this clustering algorithm.
Gets or sets the IMetricT used to compute distances between points in the clustering.
Gets or sets the maximum number of neighbors which should be used to determine the direction of the mean-shift during the computations. Default is zero (unlimited number of neighbors).
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.
Gets or sets the parallelization options for this algorithm.(Inherited from ParallelLearningBase.)
Gets or sets a cancellation token that can be used to cancel the algorithm while it is running.(Inherited from ParallelLearningBase.)
Gets or sets the relative convergence threshold for stopping the algorithm. Default is 1e-5.
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.
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.