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clustering4ever.scala.clustering.kmeans

KMeans

Related Docs: class KMeans | package kmeans

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object KMeans extends Serializable

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  1. final def !=(arg0: Any): Boolean

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  9. final def getClass(): Class[_]

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  13. final def notify(): Unit

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  14. final def notifyAll(): Unit

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  15. def run[ID, O, V <: Seq[Double], Cz[ID, O, V <: Seq[Double]] <: RealClusterizable[ID, O, V, Cz[ID, O, V]], D <: ContinuousDistance[V]](data: GenSeq[Cz[ID, O, V]], k: Int, epsilon: Double, maxIterations: Int, metric: D, initializedCenters: HashMap[Int, V] = mutable.HashMap.empty[Int, V])(implicit arg0: Numeric[ID], arg1: ClassTag[V]): KMeansModel[ID, O, V, Cz[ID, O, V], D]

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    Run the K-Means with any continuous distance

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

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  17. def toString(): String

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  18. final def wait(): Unit

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

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