KMeans

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1. final def !=(arg0: AnyRef): Boolean

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2. final def !=(arg0: Any): Boolean

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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5. final def ==(arg0: Any): Boolean

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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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   @Since( "0.8.0" )

8. final def asInstanceOf[T0]: T0

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9. def clone(): AnyRef

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10. final def eq(arg0: AnyRef): Boolean

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11. def equals(arg0: Any): Boolean

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12. def finalize(): Unit

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13. final def getClass(): Class[_]

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14. def hashCode(): Int

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15. final def isInstanceOf[T0]: Boolean

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16. final def ne(arg0: AnyRef): Boolean

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17. final def notify(): Unit

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18. final def notifyAll(): Unit

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19. final def synchronized[T0](arg0: ⇒ T0): T0

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20. def toString(): 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" )

25. final def wait(): Unit

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26. final def wait(arg0: Long, arg1: Int): Unit

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27. final def wait(arg0: Long): Unit

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