KCommons

abstract def run(): ClusteringModel

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def areCentersMovingEnough(kCentersBeforeUpdate: HashMap[Int, V], centers: HashMap[Int, V], epsilon: Double): Boolean

Check if centers move enough

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def clusterizedAndSaveCenters(vectorizedDataset: GenSeq[V], centers: HashMap[Int, V]): (GenSeq[(V, Int)], HashMap[Int, V])

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

lazy val kmppInitialization: (GenSeq[V], Int) ⇒ HashMap[Int, V]

Kmeans++ initialization

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def obtainMedoid(gs: GenSeq[V]): V

Compute the similarity matrix and extract point which is the closest from all other point according to its dissimilarity measure

def obtainNearestCenterID(v: V, centers: HashMap[Int, V]): ClusterID

def removeEmptyClusters(centers: HashMap[Int, V], kCentersBeforeUpdate: HashMap[Int, V], centersCardinality: HashMap[Int, Int]): Unit

Check if there are empty centers and remove them

def removeEmptyClustersAndCheckIfallCentersHaveConverged(centers: HashMap[Int, V], kCentersBeforeUpdate: HashMap[Int, V], centersCardinality: HashMap[Int, Int], epsilon: Double): Boolean

def resetCentersCardinality(centersCardinality: HashMap[Int, Int]): Unit

Reinitialization of cardinalities

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