scalation.analytics

Clusterer

trait Clusterer extends AnyRef

The Clusterer trait provides a common framework for several clustering algorithms.

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Known Subclasses
HierClustering, KMeansClustering, MarkovClustering
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Abstract Value Members

  1. abstract def classify(y: VectorD): Int

    Given a new point/vector y, determine which cluster it belongs to.

    Given a new point/vector y, determine which cluster it belongs to.

    y

    the vector to classify

  2. abstract def cluster(): Array[Int]

    Given a set of points/vectors, put them in clusters, returning the cluster assignment vector.

    Given a set of points/vectors, put them in clusters, returning the cluster assignment vector. A basic goal is to minimize the sum of the distances between points within each cluster.

Concrete Value Members

  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. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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    @throws( ... )

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

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  7. def equals(arg0: Any): Boolean

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  8. def finalize(): Unit

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    @throws( classOf[java.lang.Throwable] )

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

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  10. def getName(i: Int): String

    Get the name of the i-th cluster.

  11. def hashCode(): Int

    Definition Classes
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  12. final def isInstanceOf[T0]: Boolean

    Definition Classes
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  13. def name_(n: Array[String]): Unit

    Set the names for the clusters.

    Set the names for the clusters.

    n

    the array of names

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

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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

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  18. def toString(): String

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  19. final def wait(): Unit

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    @throws( ... )

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

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  21. final def wait(arg0: Long): Unit

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