KMeans_ |
Specialized KMeansTData, TDataSum for one dimensional double values.
Inheritance HierarchyNamespace: Altaxo.Calc.Clustering
Assembly: AltaxoCore (in AltaxoCore.dll) Version: 4.8.3179.0 (4.8.3179.0)
SyntaxC#
public class KMeans_Double1D : KMeans<double, double>
The KMeans_Double1D type exposes the following members.
Constructors| Name | Description | |
|---|---|---|
 | KMeans_Double1D | Initializes a new instance of the KMeans_Double1D class |
Properties| Name | Description | |
|---|---|---|
 | ClusterCounts |
Gets a list which contains the number of values in each of the clusters (length is numberOfClusters).
(Inherited from KMeansTData, TDataSum) |
| ClusterIndices |
Gets a list with the same length as the number of data points, in which each element is
the index of the cluster this data point is assigned to.
(Inherited from KMeansTData, TDataSum) |
| ClusterMeans |
Gets a list which contains the mean values of the clusters (length is numberOfClusters).
(Inherited from KMeansTData, TDataSum) |
| Data |
Gets a list which contains the data points provided.
(Inherited from KMeansTData, TDataSum) |
| HasPatchingEmptyClustersFailed |
If true, during evaluation, empty clusters have appeared, which could not be patched with other data.
(Inherited from KMeansTData, TDataSum) |
| HasReachedMaximumNumberOfIterations |
If true, the evaluation has reached the maximum number of iterations, without converging.
(Inherited from KMeansTData, TDataSum) |
| SortingOfClusterValues |
Get/sets the sorting of cluster values after evaluation. It presumes that the generic type TDataSum
implements the IComparable interface.
(Inherited from KMeansTData, TDataSum) |
Methods| Name | Description | |
|---|---|---|
| Equals | Determines whether the specified object is equal to the current object. (Inherited from Object) |
| Evaluate |
Clusters the provided data. Please note that the data are not normalized. Thus,
for multidimensional data, please normalize the data before!
(Inherited from KMeansTData, TDataSum) |
| EvaluateClustersStandardDeviation |
Evaluates for each cluster the standard deviation, i.e. the square root ( of the sum of squared distances divided by N-1)
(Inherited from KMeansTData, TDataSum) |
| EvaluateDaviesBouldinIndex(FuncTDataSum, TDataSum, Double) |
Evaluates the Davies-Bouldin-Index. The exponent q (see EvaluateDaviesBouldinIndex(FuncTDataSum, TDataSum, Double, Int32)) is set to 1,
meaning that the mean Euclidean distance of the points to their respective centroid is used in the nominator.
(Inherited from KMeansTData, TDataSum) |
| EvaluateDaviesBouldinIndex(FuncTDataSum, TDataSum, Double, Int32) |
Evaluates the Davies-Bouldin-Index. The exponent q (used to calculate the average distance of the cluster points to their centroid) can be set as parameter.
(Inherited from KMeansTData, TDataSum) |
| EvaluateMean2ndMomentOfDistances |
Evaluates for each cluster the 2nd moment of the distances, i.e. the square root of the average of the squared Euclidean distances (or whatever the distance function is) of the points to their respective centroid.
(Inherited from KMeansTData, TDataSum) |
| EvaluateMeanDistances |
Evaluates for each cluster the mean distance, i.e. the average of the Euclidean distances (or whatever the distance function is) of the points to their respective centroid.
(Inherited from KMeansTData, TDataSum) |
| EvaluateMeanNthMomentOfDistances |
Evaluates for each cluster the mean distance, i.e. the square root ( of the sum of squared distances divided by N)
(Inherited from KMeansTData, TDataSum) |
| EvaluateSumOfSquaredDistancesToClusterMean |
Evaluates the sum of (squared distance of each point to its cluster center).
(Inherited from KMeansTData, TDataSum) |
 | Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object) |
| GetFarthestPoint |
Returns the index of the point that is farthest outside the mean of its cluster.
(Inherited from KMeansTData, TDataSum) |
| GetFarthestPointOfBiggestCluster |
Returns the index of the point that is farthest outside the mean of its cluster.
(Inherited from KMeansTData, TDataSum) |
| GetHashCode | Serves as the default hash function. (Inherited from Object) |
| GetType | Gets the Type of the current instance. (Inherited from Object) |
| InitializeCentroidsAtRandom |
Initialize the centroids with a randomly choosen value from the data set.
Initializing in this way gives poor clustering results, thus we don't use it currently.
Advantage: much faster than InitializeCentroidsUsingKMeansPlusPlus.
(Inherited from KMeansTData, TDataSum) |
| InitializeCentroidsUsingKMeansPlusPlus |
Initializes the cluster mean values using the KMeans++ procedure (http://en.wikipedia.org/wiki/K-means%2B%2B).
This is slower than InitializeCentroidsAtRandom, but after initializing in this way, significant lesser iterations
are neccessary.
(Inherited from KMeansTData, TDataSum) |
| InternalEvaluateMeanNthMomentOfDistances |
Evaluates for each cluster the 2nd moment of the distances, i.e. the square root of the average of the squared Euclidean distances (or whatever the distance function is) of the points to their respective centroid.
(Inherited from KMeansTData, TDataSum) |
| MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object) |
| PatchEmptyClusters |
Tries to fill up empty clusters, by searching the biggest cluster, and then use the farthest point
from that biggest cluster to move to the empty cluster.
(Inherited from KMeansTData, TDataSum) |
| ToString | Returns a string that represents the current object. (Inherited from Object) |
| TryEvaluate |
Clusters the provided data. Please note that the data are not normalized. Thus,
for multidimensional data, please normalize the data before!
(Inherited from KMeansTData, TDataSum) |
| UpdateClusterMeanValues |
Calculates the mean values of the clusters from the data points.
(Inherited from KMeansTData, TDataSum) |
| UpdateEachPointsMembership |
Iterates over each data point, and determines to which cluster it belongs.
(Inherited from KMeansTData, TDataSum) |
See Also