EpsilonProximityModelAncestor

abstract val algorithmID: ClusteringAlgorithmNature

abstract val clusterizedVectorsSortedByID: RDD[(Long, (V, Int))]

The clusterized dataset in its basic form RDD[ID, (Vector, ClusterID)]

abstract val clustersNumber: Int

The number of generated clusters

abstract val inputDataHashCode: Int

The hashcode of input dataset to prevent missuse of some methods

abstract val metric: D

The metric used in this algorithm/model

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

General methods to obtain a clustering from the model in order to measure performances scores

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

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 ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

final def obtainClusteringIDs[O, Cz[Y, Z <: GVector[Z]] <: Clusterizable[Y, Z, Cz]](data: RDD[Cz[O, V]])(implicit ct: ClassTag[Cz[O, V]]): RDD[ClusterID]

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