nak.cluster

Kmeans

class Kmeans[T] extends Logging

A class for computing clusters for a set of points using k-means (specifically, Lloyd's algorithm).

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Instance Constructors

  1. new Kmeans(points: IndexedSeq[T], distanceFun: (T, T) ⇒ Double = Kmeans.euclideanDistance, minChangeInDispersion: Double = 1.0E-4, maxIterations: Int = 100, fixedSeedForRandom: Boolean = false)(implicit space: MutableInnerProductSpace[T, Double])

    points

    the set of points to be clustered

    maxIterations

    the maximum number of iterations to run k-means for

Value Members

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

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  7. def clone(): AnyRef

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  8. def computeClusterMemberships(centroids: IndexedSeq[T]): (Double, IndexedSeq[Int])

    Given a sequence of centroids, compute the cluster memberships for each point.

    Given a sequence of centroids, compute the cluster memberships for each point.

    centroids

    A sequence of points representing centroids.

    returns

    A pair, the first element of which is the dispersion given these centroids, and the second of which is the list of centroid indices for each of the points being clustered (based on the nearest centroid to each).

  9. final def eq(arg0: AnyRef): Boolean

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  11. def finalize(): Unit

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  15. lazy val logger: Logger

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  16. final def ne(arg0: AnyRef): Boolean

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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  19. def run(k: Int, restarts: Int = 25): (Double, IndexedSeq[T])

    Run the k-means algorithm on this set of points for some given k.

    Run the k-means algorithm on this set of points for some given k.

    k

    The number of clusters to produce.

    restarts

    The number of times to run k-means from different random starting points.

    returns

    A pair, the first element of which is the dispersion for the best set of centroids found, and the second element of which is that set of centroids.

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

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