org.apache.spark.mllib.clustering

KMeansModel

class KMeansModel extends Serializable

A clustering model for K-means. Each point belongs to the cluster with the closest center.

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StreamingKMeansModel
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Instance Constructors

  1. new KMeansModel(clusterCenters: Array[Vector])

Value Members

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

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  2. final def ##(): Int

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

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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  6. val clusterCenters: Array[Vector]

  7. def computeCost(data: RDD[Vector]): Double

    Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.

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

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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

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  12. def hashCode(): Int

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  13. final def isInstanceOf[T0]: Boolean

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  14. def k: Int

    Total number of clusters.

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

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  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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  18. def predict(points: JavaRDD[Vector]): JavaRDD[Integer]

    Maps given points to their cluster indices.

  19. def predict(points: RDD[Vector]): RDD[Int]

    Maps given points to their cluster indices.

  20. def predict(point: Vector): Int

    Returns the cluster index that a given point belongs to.

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

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  22. def toString(): String

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  23. final def wait(): Unit

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

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

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