org.apache.spark.mllib.clustering

KMeans

object KMeans extends Serializable

Top-level methods for calling K-means clustering.

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  1. 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

  7. val RANDOM: String

  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(): java.lang.Class[_]

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. def main(args: Array[String]): Unit

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

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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  20. def readResolve(): AnyRef

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

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  22. def toString(): String

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  23. def train(data: RDD[Array[Double]], k: Int, maxIterations: Int): KMeansModel

  24. def train(data: RDD[Array[Double]], k: Int, maxIterations: Int, runs: Int): KMeansModel

  25. def train(data: RDD[Array[Double]], k: Int, maxIterations: Int, runs: Int, initializationMode: String): KMeansModel

  26. final def wait(): Unit

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

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

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