Class/Object

org.clustering4ever.clustering.chaining

BigDataClusteringChaining

Related Docs: object BigDataClusteringChaining | package chaining

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case class BigDataClusteringChaining[O, V <: GVector[V], Vecto[A, B <: GVector[B]] <: VectorizationDistributed[A, B, Vecto], Cz[Y, Z <: GVector[Z]] <: Clusterizable[Y, Z, Cz]](data: RDD[Cz[O, V]], chainableID: Int, currentVectorization: Vecto[O, V], clusteringInformations: HMap[ClusteringInformationsMapping] = ...)(implicit ct: ClassTag[Cz[O, V]]) extends ClusteringChaining[O, V, Cz, Vecto[O, V], RDD] with Product with Serializable

This classe intend to run many distributed algorithms in Spark sequentially through a distributed system for large size datasets

Linear Supertypes
Product, Equals, ClusteringChaining[O, V, Cz, Vecto[O, V], RDD], DataExplorator[O, V, Cz, RDD], CollectionNature[RDD], ClusteringSharedTypes, Serializable, Serializable, AnyRef, Any
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Inherited
  1. BigDataClusteringChaining
  2. Product
  3. Equals
  4. ClusteringChaining
  5. DataExplorator
  6. CollectionNature
  7. ClusteringSharedTypes
  8. Serializable
  9. Serializable
  10. AnyRef
  11. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new BigDataClusteringChaining(data: RDD[Cz[O, V]], chainableID: Int, currentVectorization: Vecto[O, V], clusteringInformations: HMap[ClusteringInformationsMapping] = ...)(implicit ct: ClassTag[Cz[O, V]])

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Type Members

  1. final type ClusterID = Int

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    Definition Classes
    ClusteringSharedTypes
  2. final type Self[GV <: GVector[GV], OtherVecto[A, B <: GVector[B]] <: VectorizationDistributed[A, B, OtherVecto]] = BigDataClusteringChaining[O, GV, OtherVecto, Cz]

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Value Members

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

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

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

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    Definition Classes
    AnyRef → Any
  4. final def addVectorizationOnData[GV <: GVector[GV], OtherVecto[A, B <: GVector[B]] <: VectorizationDistributed[A, B, OtherVecto]](vectorization: OtherVecto[O, GV]): Self[V, Vecto]

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    Add a vectorization in ClusteringChaining object which can be applied latter

  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  6. final val chainableID: Int

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    Definition Classes
    BigDataClusteringChaining → ClusteringChaining
  7. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. final val clusteringInformations: HMap[ClusteringInformationsMapping]

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  9. val clusteringRunNumber: ClusteringRunNumber

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    Definition Classes
    ClusteringChaining
  10. implicit final val ct: ClassTag[Cz[O, V]]

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    Definition Classes
    BigDataClusteringChaining → ClusteringChaining
  11. final val currentVectorization: Vecto[O, V]

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    Definition Classes
    BigDataClusteringChaining → ClusteringChaining
  12. final val data: RDD[Cz[O, V]]

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    Definition Classes
    BigDataClusteringChaining → DataExplorator
  13. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  14. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  15. final def fusionChainable(ccl: Self[V, Vecto]): Self[V, Vecto]

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  16. val fusionChainableSecurity: Int

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    A securty value in order to allow proper reduce of Chaining models

    A securty value in order to allow proper reduce of Chaining models

    Attributes
    protected[org.clustering4ever.clustering.chaining]
    Definition Classes
    BigDataClusteringChaining → ClusteringChaining
  17. final def getClass(): Class[_]

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

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

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

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

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    Definition Classes
    AnyRef
  22. final def runAlgorithm(algorithm: ClusteringAlgorithmDistributed[V, _ <: ClusteringModelDistributed[V]]): Self[V, Vecto]

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    Run one algorithm defined by user

  23. final def runAlgorithms(algorithms: ClusteringAlgorithmDistributed[V, _ <: ClusteringModelDistributed[V]]*): Self[V, Vecto]

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    Run multiples algorithms defined by user

  24. final def switchForExistingVectorization[GV <: GVector[GV], OtherVecto[A, B <: GVector[B]] <: VectorizationDistributed[A, B, OtherVecto]](vectorization: OtherVecto[O, GV])(implicit ct: ClassTag[Cz[O, GV]]): Self[GV, OtherVecto]

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    Update the current working vector for another existing vector in clusterizable vectorized field

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

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    Definition Classes
    AnyRef
  26. final def updateVectorization[GV <: GVector[GV], OtherVecto[A, B <: GVector[B]] <: VectorizationDistributed[A, B, OtherVecto]](vectorization: OtherVecto[O, GV])(implicit ct: ClassTag[Cz[O, GV]]): Self[GV, OtherVecto]

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    Update working vector with given vectorization without saving neither previous working vector and new vector in clusterizable vectorized field to prevent clusterizable to becomes heavier and then slowing down clustering algorithms

  27. val vectorizations: HMap[VectorizationMapping]

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    HMap of original and added EasyVectorizationDistributed

    HMap of original and added EasyVectorizationDistributed

    Definition Classes
    BigDataClusteringChaining → ClusteringChaining
  28. final def wait(): Unit

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

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Product

Inherited from Equals

Inherited from ClusteringChaining[O, V, Cz, Vecto[O, V], RDD]

Inherited from DataExplorator[O, V, Cz, RDD]

Inherited from CollectionNature[RDD]

Inherited from ClusteringSharedTypes

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped