Class

org.apache.spark.ml.clustering

ClusteringSummary

Related Doc: package clustering

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class ClusteringSummary extends Serializable

:: Experimental :: Summary of clustering algorithms.

Annotations
@Experimental()
Source
ClusteringSummary.scala
Linear Supertypes
Serializable, Serializable, AnyRef, Any
Known Subclasses
BisectingKMeansSummary, GaussianMixtureSummary, KMeansSummary
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  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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    @throws( ... )

  6. lazy val cluster: DataFrame

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    Cluster centers of the transformed data.

  7. lazy val clusterSizes: Array[Long]

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    Size of (number of data points in) each cluster.

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

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  9. def equals(arg0: Any): Boolean

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  10. val featuresCol: String

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    Name for column of features in predictions.

  11. def finalize(): Unit

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

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  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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  15. val k: Int

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    Number of clusters.

  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. val numIter: Int

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    Number of iterations.

    Number of iterations.

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    @Since( "2.4.0" )

  20. val predictionCol: String

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    Name for column of predicted clusters in predictions.

  21. val predictions: DataFrame

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    DataFrame produced by model.transform().

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

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  23. def toString(): String

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  24. final def wait(): Unit

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

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

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