org.apache.spark.mllib.clustering

KMeans

object KMeans extends Serializable

Top-level methods for calling K-means clustering.

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@Since( "0.8.0" )
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KMeans.scala
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  6. val K_MEANS_PARALLEL: String

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    @Since( "0.8.0" )
  7. val RANDOM: String

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  21. def train(data: RDD[Vector], k: Int, maxIterations: Int, runs: Int): KMeansModel

    Trains a k-means model using specified parameters and the default values for unspecified.

    Trains a k-means model using specified parameters and the default values for unspecified.

    Annotations
    @Since( "0.8.0" )
  22. def train(data: RDD[Vector], k: Int, maxIterations: Int): KMeansModel

    Trains a k-means model using specified parameters and the default values for unspecified.

    Trains a k-means model using specified parameters and the default values for unspecified.

    Annotations
    @Since( "0.8.0" )
  23. def train(data: RDD[Vector], k: Int, maxIterations: Int, runs: Int, initializationMode: String): KMeansModel

    Trains a k-means model using the given set of parameters.

    Trains a k-means model using the given set of parameters.

    data

    training points stored as RDD[Vector]

    k

    number of clusters

    maxIterations

    max number of iterations

    runs

    number of parallel runs, defaults to 1. The best model is returned.

    initializationMode

    initialization model, either "random" or "k-means||" (default).

    Annotations
    @Since( "0.8.0" )
  24. def train(data: RDD[Vector], k: Int, maxIterations: Int, runs: Int, initializationMode: String, seed: Long): KMeansModel

    Trains a k-means model using the given set of parameters.

    Trains a k-means model using the given set of parameters.

    data

    training points stored as RDD[Vector]

    k

    number of clusters

    maxIterations

    max number of iterations

    runs

    number of parallel runs, defaults to 1. The best model is returned.

    initializationMode

    initialization model, either "random" or "k-means||" (default).

    seed

    random seed value for cluster initialization

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    @Since( "1.3.0" )
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