Class/Object

org.clustering4ever.clustering.kcenters.rdd

KModes

Related Docs: object KModes | package rdd

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final case class KModes[D <: BinaryDistance](k: Int, metric: D, minShift: Double, maxIterations: Int, persistanceLVL: StorageLevel = StorageLevel.MEMORY_ONLY, customCenters: HashMap[Int, BinaryVector] = ...)(implicit ctV: ClassTag[BinaryVector]) extends KCentersAncestor[BinaryVector, D, KModesModel[D]] with Product with Serializable

Linear Supertypes
Product, Equals, KCentersAncestor[BinaryVector, D, KModesModel[D]], ClusteringAlgorithmDistributed[BinaryVector, KModesModel[D]], ClusteringAlgorithm, KCommonsSpark[BinaryVector, D], KCommons[BinaryVector, D], ClusteringSharedTypes, KCommonsArgs[BinaryVector, D], MetricArgs[BinaryVector, D], KArgs, MaxIterationsArgs, MinShiftArgs, AlgorithmsArguments, Serializable, Serializable, AnyRef, Any
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Inherited
  1. KModes
  2. Product
  3. Equals
  4. KCentersAncestor
  5. ClusteringAlgorithmDistributed
  6. ClusteringAlgorithm
  7. KCommonsSpark
  8. KCommons
  9. ClusteringSharedTypes
  10. KCommonsArgs
  11. MetricArgs
  12. KArgs
  13. MaxIterationsArgs
  14. MinShiftArgs
  15. AlgorithmsArguments
  16. Serializable
  17. Serializable
  18. AnyRef
  19. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new KModes(k: Int, metric: D, minShift: Double, maxIterations: Int, persistanceLVL: StorageLevel = StorageLevel.MEMORY_ONLY, customCenters: HashMap[Int, BinaryVector] = ...)(implicit ctV: ClassTag[BinaryVector])

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Type Members

  1. final type ClusterID = Int

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    Definition Classes
    ClusteringSharedTypes

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 val algorithmID: extensibleAlgorithmNature.KModes.type

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    Definition Classes
    KModes → ClusteringAlgorithm

  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  6. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  7. implicit final val ctV: ClassTag[BinaryVector]

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    Attributes
    protected
    Definition Classes
    KModes → KCommonsSpark

  8. final val customCenters: HashMap[Int, BinaryVector]

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    Definition Classes
    KModes → KCommons

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

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    Definition Classes
    AnyRef

  10. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  11. final def fit[O, Cz[Y, Z <: GVector[Z]] <: Clusterizable[Y, Z, Cz]](data: RDD[Cz[O, BinaryVector]])(implicit ct: ClassTag[Cz[O, BinaryVector]]): KModesModel[D]

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    Execute the corresponding clustering algorithm

    Execute the corresponding clustering algorithm

    returns

    ClusteringModel

    Definition Classes
    KModes → ClusteringAlgorithmDistributed

  12. implicit val getCenter: (BinaryVector, Long) ⇒ BinaryVector

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    Definition Classes
    KModes → KCentersAncestor

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

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    Definition Classes
    AnyRef → Any

  14. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  15. final val k: Int

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    Definition Classes
    KModes → KCommonsArgs → KArgs

  16. final def kmppInitializationRDD(vectorizedDataset: RDD[BinaryVector], k: Int, metric: D): HashMap[Int, BinaryVector]

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    To upgrade Kmeans++ initialization

    To upgrade Kmeans++ initialization

    References

    • Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman, and Angela Y. Wu. An Efficient k-Means Clustering Algorithm: Analysis and Implementation. IEEE TRANS. PAMI, 2002.
    • D. Arthur and S. Vassilvitskii. "K-means++: the advantages of careful seeding". ACM-SIAM symposium on Discrete algorithms, 1027-1035, 2007.
    • Anna D. Peterson, Arka P. Ghosh and Ranjan Maitra. A systematic evaluation of different methods for initializing the K-means clustering algorithm. 2010.
    Attributes
    protected
    Definition Classes
    KCommonsSpark

  17. final val maxIterations: Int

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    Definition Classes
    KModes → MaxIterationsArgs

  18. final val metric: D

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    Definition Classes
    KModes → MetricArgs

  19. final val minShift: Double

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    Definition Classes
    KModes → KCommonsArgs → MinShiftArgs

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

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    Definition Classes
    AnyRef

  21. final def notify(): Unit

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    Definition Classes
    AnyRef

  22. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  23. final def obtainCenters[O, Cz[Y, Z <: GVector[Z]] <: Clusterizable[Y, Z, Cz]](data: RDD[Cz[O, BinaryVector]])(implicit ct: ClassTag[Cz[O, BinaryVector]]): HashMap[Int, BinaryVector]

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    Attributes
    protected
    Definition Classes
    KCentersAncestor

  24. final def parallelKmPPInitialization(vectorizedDataset: RDD[BinaryVector], k: Int, metric: D, l: Int = 10, numIter: Int = 5): HashMap[Int, BinaryVector]

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    Definition Classes
    KCommonsSpark

  25. final val persistanceLVL: StorageLevel

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    Definition Classes
    KModes → KCommonsSpark

  26. final def randomSelectedInitializationRDD(vectorizedDataset: RDD[BinaryVector], k: Int): HashMap[Int, BinaryVector]

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    Select randomly k points which will becomes k centers itinialization.

    Select randomly k points which will becomes k centers itinialization.

    Attributes
    protected
    Definition Classes
    KCommonsSpark

  27. implicit val sumVector: (BinaryVector, BinaryVector) ⇒ BinaryVector

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    Definition Classes
    KModes → KCentersAncestor

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

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    Definition Classes
    AnyRef

  29. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Product

Inherited from Equals

Inherited from KCentersAncestor[BinaryVector, D, KModesModel[D]]

Inherited from ClusteringAlgorithmDistributed[BinaryVector, KModesModel[D]]

Inherited from ClusteringAlgorithm

Inherited from KCommonsSpark[BinaryVector, D]

Inherited from KCommons[BinaryVector, D]

Inherited from ClusteringSharedTypes

Inherited from KCommonsArgs[BinaryVector, D]

Inherited from MetricArgs[BinaryVector, D]

Inherited from KArgs

Inherited from MaxIterationsArgs

Inherited from MinShiftArgs

Inherited from AlgorithmsArguments

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped