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org.emmalanguage.lib.ml.clustering

kMeans

Related Doc: package clustering

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object kMeans

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  1. type Solution[PID] = LDPoint[PID, DPoint[PID]]

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

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. def apply[PID](D: Int, k: Int, runs: Int, iterations: Int, distance: (DVector, DVector) ⇒ Double = sqdist, seed: Long = 452642543145L)(points: DataBag[DPoint[PID]])(implicit arg0: Meta[PID]): DataBag[Solution[PID]]

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    K-Means clustering algorithm.

    K-Means clustering algorithm.

    PID

    Point identity type.

    D

    Number of dimensions

    k

    Number of clusters

    runs

    Number of runs.

    iterations

    Number of iterations.

    distance

    Distance metric.

    seed

    Centroids seed.

    points

    Input points.

  5. final def asInstanceOf[T0]: T0

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