KMeans

Instance Constructors

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

k
: number of clusters
metric
: a defined dissimilarity measure
minShift
: minimal threshold under which we consider a centroid has converged
maxIterations
: maximal number of iteration

Type Members

final type ClusterID = Int

Definition Classes
ClusteringSharedTypes

Value Members

final def !=(arg0: Any): Boolean

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

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

Definition Classes
AnyRef → Any
final val algorithmID: extensibleAlgorithmNature.KMeans.type

Definition Classes
KMeans → ClusteringAlgorithm
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
implicit final val ctV: ClassTag[ScalarVector]

Attributes
protected
Definition Classes
KMeans → KCommonsSpark
final val customCenters: HashMap[Int, ScalarVector]

Definition Classes
KMeans → KCommons
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def fit[O, Cz[Y, Z <: GVector[Z]] <: Clusterizable[Y, Z, Cz]](data: RDD[Cz[O, ScalarVector]])(implicit ct: ClassTag[Cz[O, ScalarVector]]): KMeansModel[D]

Execute the corresponding clustering algorithm
Execute the corresponding clustering algorithm
returns
ClusteringModel

Definition Classes
KMeans → ClusteringAlgorithmDistributed
implicit val getCenter: (ScalarVector, Long) ⇒ ScalarVector

Definition Classes
KMeans → KCentersAncestor
final def getClass(): Class[_]

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

Definition Classes
Any
final val k: Int

: number of clusters
: number of clusters

Definition Classes
KMeans → KCommonsArgs → KArgs
final def kmppInitializationRDD(vectorizedDataset: RDD[ScalarVector], k: Int, metric: D): HashMap[Int, ScalarVector]

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
final val maxIterations: Int

: maximal number of iteration
: maximal number of iteration

Definition Classes
KMeans → MaxIterationsArgs
final val metric: D

: a defined dissimilarity measure
: a defined dissimilarity measure

Definition Classes
KMeans → MetricArgs
final val minShift: Double

: minimal threshold under which we consider a centroid has converged
: minimal threshold under which we consider a centroid has converged

Definition Classes
KMeans → KCommonsArgs → MinShiftArgs
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
final def obtainCenters[O, Cz[Y, Z <: GVector[Z]] <: Clusterizable[Y, Z, Cz]](data: RDD[Cz[O, ScalarVector]])(implicit ct: ClassTag[Cz[O, ScalarVector]]): HashMap[Int, ScalarVector]

Attributes
protected
Definition Classes
KCentersAncestor
final def parallelKmPPInitialization(vectorizedDataset: RDD[ScalarVector], k: Int, metric: D, l: Int = 10, numIter: Int = 5): HashMap[Int, ScalarVector]

Definition Classes
KCommonsSpark
final val persistanceLVL: StorageLevel

Definition Classes
KMeans → KCommonsSpark
final def randomSelectedInitializationRDD(vectorizedDataset: RDD[ScalarVector], k: Int): HashMap[Int, ScalarVector]

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
implicit val sumVector: (ScalarVector, ScalarVector) ⇒ ScalarVector

Definition Classes
KMeans → KCentersAncestor
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
final def wait(): Unit

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

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

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Docs: object KMeans | package rdd

Instance Constructors

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

Type Members

final type ClusterID = Int

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final val algorithmID: extensibleAlgorithmNature.KMeans.type

final def asInstanceOf[T0]: T0

def clone(): AnyRef

implicit final val ctV: ClassTag[ScalarVector]

final val customCenters: HashMap[Int, ScalarVector]

final def eq(arg0: AnyRef): Boolean

def finalize(): Unit

final def fit[O, Cz[Y, Z <: GVector[Z]] <: Clusterizable[Y, Z, Cz]](data: RDD[Cz[O, ScalarVector]])(implicit ct: ClassTag[Cz[O, ScalarVector]]): KMeansModel[D]

implicit val getCenter: (ScalarVector, Long) ⇒ ScalarVector

final def getClass(): Class[_]

final def isInstanceOf[T0]: Boolean

final val k: Int

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

References

final val maxIterations: Int

final val metric: D

final val minShift: Double

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

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

final val persistanceLVL: StorageLevel

final def randomSelectedInitializationRDD(vectorizedDataset: RDD[ScalarVector], k: Int): HashMap[Int, ScalarVector]

implicit val sumVector: (ScalarVector, ScalarVector) ⇒ ScalarVector

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

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Product

Inherited from Equals

Inherited from KCentersAncestor[ScalarVector, D, KMeansModel[D]]

Inherited from ClusteringAlgorithmDistributed[ScalarVector, KMeansModel[D]]

Inherited from ClusteringAlgorithm

Inherited from KCommonsSpark[ScalarVector, D]

Inherited from KCommons[ScalarVector, D]

Inherited from ClusteringSharedTypes

Inherited from KCommonsArgs[ScalarVector, D]

Inherited from MetricArgs[ScalarVector, 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