|
KModes Properties |
The KModes type exposes the following members.
Properties| Name | Description | |
|---|---|---|
 | Clusters |
Gets the clusters found by K-modes.
(Inherited from KModesT.) |
| 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.
(Inherited from KModesT.) |
| Dimension |
Gets the dimensionality of the data space.
(Inherited from KModesT.) |
| Distance |
Gets or sets the distance function used
as a distance metric between data points.
(Inherited from KModesT.) |
| 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.
(Inherited from KModesT.) |
| Initialization |
Gets or sets the strategy used to initialize the
centroids of the clustering algorithm. Default is
KMeansPlusPlus.
(Inherited from KModesT.) |
| Iterations |
Gets the number of iterations performed in the
last call to this class' Compute methods.
(Inherited from KModesT.) |
| K |
Gets the number of clusters.
(Inherited from KModesT.) |
| 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.
(Inherited from KModesT.) |
| 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.
(Inherited from KModesT.) |
See Also