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object KMeans extends Serializable

Top-level methods for calling K-means clustering.

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@Since( "0.8.0" )
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  4. val K_MEANS_PARALLEL: String
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  5. val RANDOM: String
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  19. 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.

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    @Since( "0.8.0" )
  20. def train(data: RDD[Vector], k: Int, maxIterations: 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 as an RDD of Vector types.

    k

    Number of clusters to create.

    maxIterations

    Maximum number of iterations allowed.

    initializationMode

    The initialization algorithm. This can either be "random" or "k-means||". (default: "k-means||")

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    @Since( "2.1.0" )
  21. def train(data: RDD[Vector], k: Int, maxIterations: 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 as an RDD of Vector types.

    k

    Number of clusters to create.

    maxIterations

    Maximum number of iterations allowed.

    initializationMode

    The initialization algorithm. This can either be "random" or "k-means||". (default: "k-means||")

    seed

    Random seed for cluster initialization. Default is to generate seed based on system time.

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    @Since( "2.1.0" )
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