org.apache.spark.mllib.clustering

KMeans

object KMeans extends Serializable

Top-level methods for calling K-means clustering.

Linear Supertypes
Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. KMeans
  2. Serializable
  3. Serializable
  4. AnyRef
  5. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Value Members

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

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

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
    AnyRef → Any
  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any
  6. val K_MEANS_PARALLEL: String

  7. val RANDOM: String

  8. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  9. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  10. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  11. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  12. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  13. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  14. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  15. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  16. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  17. final def notify(): Unit

    Definition Classes
    AnyRef
  18. final def notifyAll(): Unit

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

    Definition Classes
    AnyRef
  20. def toString(): String

    Definition Classes
    AnyRef → Any
  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.

  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.

  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[Array[Double]]

    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).

  24. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  25. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  26. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped