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ClusterData_KMeans
Clusters data using KMeans algorithm.
Syntax
C++
C#
void avl::ClusterData_KMeans
(
const atl::Array<atl::Array<float> >& inData,
const int inClusters,
const int inMaxIterations,
const int inSeed,
const float inTerminationFactor,
const avl::KMeansClusteringMethod::Type inClusteringMethod,
avl::Matrix& outCentroids,
atl::Array<int>& outPointToClusterAssignment,
float& outDistanceSum
)
void ClusterData_KMeans
(
float[][] inData,
int inClusters,
int inMaxIterations,
int inSeed,
float inTerminationFactor,
KMeansClusteringMethod inClusteringMethod,
out Matrix outCentroids,
out int[] outPointToClusterAssignment,
out float outDistanceSum
)
Parameters
|
Name |
Type |
Range |
Default |
Description |
|
inData |
const Array<Array<float> >& |
|
|
Data set, array of examples |
|
inClusters |
const int |
2 - + |
2 |
Number of clusters to extract |
|
inMaxIterations |
const int |
10 - 1000 |
200 |
Maximal number of procedure iterations |
|
inSeed |
const int |
0 - |
5489 |
Seed to init random engine |
|
inTerminationFactor |
const float |
1.0 - 2.0 |
1.5f |
Additional factor of procedure stop |
|
inClusteringMethod |
const KMeansClusteringMethod::Type |
|
KMeansPlusPlus |
KMeans variant to use |
|
outCentroids |
Matrix& |
|
|
Resulting centroid points in feature space |
|
outPointToClusterAssignment |
Array<int>& |
|
|
Array of input point assignments to generated clusters |
|
outDistanceSum |
float& |
|
|
Sum of squared distances from points to its respective cluster centroids |
Errors
Error type |
Description |
DomainError |
Empty dataset on input in ClusterData_KMeans. |
DomainError |
Cannot make more clusters than there is data in input dataset in ClusterData_KMeans. |
DomainError |
Inconsistent number of data coordinates in input dataset in ClusterData_KMeans. |