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Clusters data using KMeans algorithm.
Namespace: | Fil |
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Assembly: | FilNet.dll |
Syntax
C++
C#
public static void ClusterData_KMeans ( List<List<float>> inData, int inClusters, int inMaxIterations, int inSeed, float inTerminationFactor, Fil.KMeansClusteringMethod inClusteringMethod, Fil.Matrix outCentroids, List<int> outPointToClusterAssignment, out float outDistanceSum )
Parameters
Name | Type | Range | Default | Description | |
---|---|---|---|---|---|
![]() | inData | System.Collections.Generic.List<System.Collections.Generic.List<float>> | Data set, array of examples. | ||
![]() | inClusters | int | <2, +INF> | 2 | Number of clusters to extract. Default value: 2. |
![]() | inMaxIterations | int | <10, 1000> | 200 | Maximal number of procedure iterations. Default value: 200. |
![]() | inSeed | int | <0, INF> | 5489 | Seed to init random engine. Default value: 5489. |
![]() | inTerminationFactor | float | <1.0f, 2.0f> | 1.5f | Additional factor of procedure stop. Default value: 1.5f. |
![]() | inClusteringMethod | Fil.KMeansClusteringMethod | KMeansPlusPlus | KMeans variant to use. Default value: KMeansPlusPlus. | |
![]() | outCentroids | Fil.Matrix | Resulting centroid points in feature space. | ||
![]() | outPointToClusterAssignment | System.Collections.Generic.List<int> | Array of input point assignments to generated clusters. | ||
![]() | outDistanceSum | float | Sum of squared distances from points to its respective cluster centroids. |