class KMeans extends Serializable with Logging
K-means clustering with a k-means++ like initialization mode (the k-means|| algorithm by Bahmani et al).
This is an iterative algorithm that will make multiple passes over the data, so any RDDs given to it should be cached by the user.
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- KMeans.scala
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new
KMeans()
Constructs a KMeans instance with default parameters: {k: 2, maxIterations: 20, initializationMode: "k-means||", initializationSteps: 2, epsilon: 1e-4, seed: random, distanceMeasure: "euclidean"}.
Constructs a KMeans instance with default parameters: {k: 2, maxIterations: 20, initializationMode: "k-means||", initializationSteps: 2, epsilon: 1e-4, seed: random, distanceMeasure: "euclidean"}.
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def
getDistanceMeasure: String
The distance suite used by the algorithm.
The distance suite used by the algorithm.
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def
getEpsilon: Double
The distance threshold within which we've consider centers to have converged.
The distance threshold within which we've consider centers to have converged.
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def
getInitializationMode: String
The initialization algorithm.
The initialization algorithm. This can be either "random" or "k-means||".
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def
getInitializationSteps: Int
Number of steps for the k-means|| initialization mode
Number of steps for the k-means|| initialization mode
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def
getK: Int
Number of clusters to create (k).
Number of clusters to create (k).
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It is possible for fewer than k clusters to be returned, for example, if there are fewer than k distinct points to cluster.
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def
getMaxIterations: Int
Maximum number of iterations allowed.
Maximum number of iterations allowed.
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def
getSeed: Long
The random seed for cluster initialization.
The random seed for cluster initialization.
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initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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def
run(data: RDD[Vector]): KMeansModel
Train a K-means model on the given set of points;
data
should be cached for high performance, because this is an iterative algorithm.Train a K-means model on the given set of points;
data
should be cached for high performance, because this is an iterative algorithm.- Annotations
- @Since( "0.8.0" )
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def
setDistanceMeasure(distanceMeasure: String): KMeans.this.type
Set the distance suite used by the algorithm.
Set the distance suite used by the algorithm.
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def
setEpsilon(epsilon: Double): KMeans.this.type
Set the distance threshold within which we've consider centers to have converged.
Set the distance threshold within which we've consider centers to have converged. If all centers move less than this Euclidean distance, we stop iterating one run.
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def
setInitialModel(model: KMeansModel): KMeans.this.type
Set the initial starting point, bypassing the random initialization or k-means|| The condition model.k == this.k must be met, failure results in an IllegalArgumentException.
Set the initial starting point, bypassing the random initialization or k-means|| The condition model.k == this.k must be met, failure results in an IllegalArgumentException.
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def
setInitializationMode(initializationMode: String): KMeans.this.type
Set the initialization algorithm.
Set the initialization algorithm. This can be either "random" to choose random points as initial cluster centers, or "k-means||" to use a parallel variant of k-means++ (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
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def
setInitializationSteps(initializationSteps: Int): KMeans.this.type
Set the number of steps for the k-means|| initialization mode.
Set the number of steps for the k-means|| initialization mode. This is an advanced setting -- the default of 2 is almost always enough. Default: 2.
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def
setK(k: Int): KMeans.this.type
Set the number of clusters to create (k).
Set the number of clusters to create (k).
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It is possible for fewer than k clusters to be returned, for example, if there are fewer than k distinct points to cluster. Default: 2.
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def
setMaxIterations(maxIterations: Int): KMeans.this.type
Set maximum number of iterations allowed.
Set maximum number of iterations allowed. Default: 20.
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def
setSeed(seed: Long): KMeans.this.type
Set the random seed for cluster initialization.
Set the random seed for cluster initialization.
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