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              KMedoidsT Properties | 
          
The KMedoidsT generic type exposes the following members.
| Name | Description | |
|---|---|---|
| Clusters | 
              Gets the clusters found by k-Medoids.
              | |
| ComputeError | 
              Gets or sets whether the clustering distortion error (the
              average distance between all data points and the cluster
              centroids) should be computed at the end of the algorithm.
              The result will be stored in Error. Default is true.
              | |
| Dimension | 
              Gets the dimensionality of the data space.
              | |
| Distance | 
              Gets or sets the distance function used
              as a distance metric between data points.
              | |
| Error | 
              Gets the cluster distortion error (the average distance 
              between data points and the cluster centroids) after the 
              last call to this class' Compute methods.
              | |
| Initialization | 
              Gets or sets the strategy used to initialize the
              centroids of the clustering algorithm. Default is
              PamBuild.
              | |
| Iterations | 
              Gets the number of iterations performed in the
              last call to this class' Compute methods.
              | |
| K | 
              Gets the number of clusters.
              | |
| 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 1e-5.
              |