InternalIndicesAncestorLocal

abstract val metric: D

The employed metric

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

final def ballHall[O, Cz[B, C <: GVector[C]] <: Clusterizable[B, C, Cz]](clusterized: GenSeq[Cz[O, V]], clusteringNumber: Int): Double

def clone(): AnyRef

final def clustersIDsNumber[O, Cz[B, C <: GVector[C]] <: Clusterizable[B, C, Cz]](clusterized: GenSeq[Cz[O, V]], clusteringNumber: Int): ArrayBuffer[Int]

final def daviesBouldin[O, Cz[B, C <: GVector[C]] <: Clusterizable[B, C, Cz]](clusterized: GenSeq[Cz[O, V]], clusteringNumber: Int): Double

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

final def getClass(): Class[_]

final def good(scatter1: Double, scatter2: Double, center1: V, center2: V, metric: D): Double

Measure of how good the clustering scheme is Params: scatter1,scatter2: Double - the scatter value of cluster 1 and cluster 2 center1,center2: Array[Double] - The centroid of cluster 1 and cluster 2

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def obtainVectorsByClusterID[O, Cz[B, C <: GVector[C]] <: Clusterizable[B, C, Cz]](clusterized: GenSeq[Cz[O, V]], clusteringNumber: Int): GenMap[ClusterID, GenSeq[V]]

final def scatter(cluster: GenSeq[V], centroid: V, metric: D): Double

Scatter of point in cluster Measure average distance to centroïd

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

def toString(): String

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

final def wait(arg0: Long): Unit

final def wait(): Unit

def finalize(): Unit