org.apache.spark.streamdm.clusterers

Clustream

class Clustream extends Clusterer

A Clusterer trait defines the needed operations for any implemented clustering algorithm. It provides methods for clustering and for returning the computed cluster.

It uses the following options:

Linear Supertypes
Clusterer, Learner, Serializable, Configurable, Serializable, AnyRef, Any
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Inherited
  1. Clustream
  2. Clusterer
  3. Learner
  4. Serializable
  5. Configurable
  6. Serializable
  7. AnyRef
  8. Any
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Visibility
  1. Public
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Instance Constructors

  1. new Clustream()

Type Members

  1. type T = MicroClusters

    Definition Classes
    ClustreamLearner

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. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  7. def assign(input: DStream[Example]): DStream[(Example, Double)]

    Definition Classes
    ClustreamClusterer
  8. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. var clusters: Array[Example]

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

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

    Definition Classes
    AnyRef → Any
  12. var exampleLearnerSpecification: ExampleSpecification

  13. def finalize(): Unit

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

    Definition Classes
    AnyRef → Any
  15. def getClusters: Array[Example]

    Definition Classes
    ClustreamClusterer
  16. def getModel: MicroClusters

    Gets the current Model used for the Learner.

    Gets the current Model used for the Learner.

    returns

    the Model object used for training

    Definition Classes
    ClustreamLearner
  17. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  18. def init(exampleSpecification: ExampleSpecification): Unit

    Init the model based on the algorithm implemented in the learner.

    Init the model based on the algorithm implemented in the learner.

    exampleSpecification

    the ExampleSpecification of the input stream.

    Definition Classes
    ClustreamLearner
  19. val initOption: IntOption

  20. var initialBuffer: Array[Example]

  21. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  22. val kOption: IntOption

  23. val mcOption: IntOption

  24. var microclusters: MicroClusters

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

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

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

    Definition Classes
    AnyRef
  28. val repOption: IntOption

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

    Definition Classes
    AnyRef
  30. def toString(): String

    Definition Classes
    AnyRef → Any
  31. def train(input: DStream[Example]): Unit

    Train the model based on the algorithm implemented in the learner, from the stream of Examples given for training.

    Train the model based on the algorithm implemented in the learner, from the stream of Examples given for training.

    input

    a stream of Examples

    Definition Classes
    ClustreamLearner
  32. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Clusterer

Inherited from Learner

Inherited from Serializable

Inherited from Configurable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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