Packages

trait Clusterer extends AnyRef

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

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Known Subclasses
HierClusterer, KMeansClusterer, KMeansPPClusterer, MarkovClusterer, KMeansClustering_F
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Abstract Value Members

  1. abstract def centroids(): MatrixD

    Return the centroids (a centroid is the mean of points in a cluster).

    Return the centroids (a centroid is the mean of points in a cluster). Should only be called after 'cluster ()'.

  2. 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

  3. 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.

  4. abstract def csize(): VectorI

    Return the sizes (number of points within) of the clusters.

    Return the sizes (number of points within) of the clusters. Should only be called after 'cluster ()'.

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
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  2. final def ##(): Int
    Definition Classes
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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
    Attributes
    protected[java.lang]
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    @native() @throws( ... )
  6. val clustered: Boolean

    Flag indicating whether the points have already been clusterer

    Flag indicating whether the points have already been clusterer

    Attributes
    protected
  7. def distance(u: VectorD, v: VectorD): Double

    Compute a distance metric (e.g., distance squared) between vectors/points 'u' and 'v'.

    Compute a distance metric (e.g., distance squared) between vectors/points 'u' and 'v'. Override this methods to use a different metric, e.g., 'norm' - the Euclidean distance, 2-norm 'norm1' - the Manhattan distance, 1-norm

    u

    the first vector/point

    v

    the second vector/point

  8. final def eq(arg0: AnyRef): Boolean
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  9. def equals(arg0: Any): Boolean
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  10. def finalize(): Unit
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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  11. final def getClass(): Class[_]
    Definition Classes
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    Annotations
    @native()
  12. def getName(i: Int): String

    Get the name of the i-th cluster.

  13. def hashCode(): Int
    Definition Classes
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    Annotations
    @native()
  14. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  15. def name_(n: Array[String]): Unit

    Set the names for the clusters.

    Set the names for the clusters.

    n

    the array of names

  16. final def ne(arg0: AnyRef): Boolean
    Definition Classes
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  17. final def notify(): Unit
    Definition Classes
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    @native()
  18. final def notifyAll(): Unit
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    @native()
  19. def sse(x: MatrixD): Double

    Compute the sum of squared errors within the clusters, where error is indicated by e.g., the distance from a point to its centroid.

  20. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
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  21. def toString(): String
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  22. final def wait(): Unit
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    @throws( ... )
  23. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  24. final def wait(arg0: Long): Unit
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    @native() @throws( ... )

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