Object/Class

org.apache.spark.mllib.clustering

KMeans

Related Docs: class KMeans | package clustering

Permalink

object KMeans extends Serializable

Top-level methods for calling K-means clustering.

Annotations
@Since( "0.8.0" )
Source
KMeans.scala
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
Visibility
  1. Public
  2. All

Value Members

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

    Permalink
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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

    Permalink
    Definition Classes
    AnyRef → Any
  4. val K_MEANS_PARALLEL: String

    Permalink
    Annotations
    @Since( "0.8.0" )
  5. val RANDOM: String

    Permalink
    Annotations
    @Since( "0.8.0" )
  6. final def asInstanceOf[T0]: T0

    Permalink
    Definition Classes
    Any
  7. def clone(): AnyRef

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

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

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

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

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

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

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

    Permalink
    Definition Classes
    AnyRef
  15. final def notify(): Unit

    Permalink
    Definition Classes
    AnyRef
  16. final def notifyAll(): Unit

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

    Permalink
    Definition Classes
    AnyRef
  18. def toString(): String

    Permalink
    Definition Classes
    AnyRef → Any
  19. def train(data: RDD[Vector], k: Int, maxIterations: Int): KMeansModel

    Permalink

    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" )
  20. def train(data: RDD[Vector], k: Int, maxIterations: Int, initializationMode: String): KMeansModel

    Permalink

    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 as an RDD of Vector types.

    k

    Number of clusters to create.

    maxIterations

    Maximum number of iterations allowed.

    initializationMode

    The initialization algorithm. This can either be "random" or "k-means||". (default: "k-means||")

    Annotations
    @Since( "2.1.0" )
  21. def train(data: RDD[Vector], k: Int, maxIterations: Int, initializationMode: String, seed: Long): KMeansModel

    Permalink

    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 as an RDD of Vector types.

    k

    Number of clusters to create.

    maxIterations

    Maximum number of iterations allowed.

    initializationMode

    The initialization algorithm. This can either be "random" or "k-means||". (default: "k-means||")

    seed

    Random seed for cluster initialization. Default is to generate seed based on system time.

    Annotations
    @Since( "2.1.0" )
  22. final def wait(): Unit

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

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

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Deprecated Value Members

  1. def train(data: RDD[Vector], k: Int, maxIterations: Int, runs: Int): KMeansModel

    Permalink

    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" ) @deprecated
    Deprecated

    (Since version 2.1.0) Use train method without 'runs'

  2. def train(data: RDD[Vector], k: Int, maxIterations: Int, runs: Int, initializationMode: String): KMeansModel

    Permalink

    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 as an RDD of Vector types.

    k

    Number of clusters to create.

    maxIterations

    Maximum number of iterations allowed.

    runs

    This param has no effect since Spark 2.0.0.

    initializationMode

    The initialization algorithm. This can either be "random" or "k-means||". (default: "k-means||")

    Annotations
    @Since( "0.8.0" ) @deprecated
    Deprecated

    (Since version 2.1.0) Use train method without 'runs'

  3. def train(data: RDD[Vector], k: Int, maxIterations: Int, runs: Int, initializationMode: String, seed: Long): KMeansModel

    Permalink

    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 as an RDD of Vector types.

    k

    Number of clusters to create.

    maxIterations

    Maximum number of iterations allowed.

    runs

    This param has no effect since Spark 2.0.0.

    initializationMode

    The initialization algorithm. This can either be "random" or "k-means||". (default: "k-means||")

    seed

    Random seed for cluster initialization. Default is to generate seed based on system time.

    Annotations
    @Since( "1.3.0" ) @deprecated
    Deprecated

    (Since version 2.1.0) Use train method without 'runs'

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped