KCentersAncestor

abstract val algorithmID: ClusteringAlgorithmNature

implicit abstract val ctV: ClassTag[V]

abstract val customCenters: HashMap[Int, V]

abstract def fit[O, Cz[Y, Z <: GVector[Z]] <: Clusterizable[Y, Z, Cz]](data: Dataset[Cz[O, V]])(implicit ct: ClassTag[Cz[O, V]]): CA

Execute the corresponding clustering algorithm

abstract val k: Int

abstract val kryoSerialization: Boolean

abstract val maxIterations: Int

abstract val metric: D

abstract val minShift: Double

abstract val persistanceLVL: StorageLevel

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final val encoder: Encoder[(Int, V)]

kryo Serialization if true, java one else

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

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

To upgrade Kmeans++ initialization

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: Dataset[Cz[O, V]])(implicit ct: ClassTag[Cz[O, V]]): HashMap[Int, V]

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

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

Select randomly k points which will becomes k centers itinialization.

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit