class KMeansPPClusterer extends Clusterer with Error

The KMeansPPClusterer class cluster several vectors/points using the k-means++ clustering technique. -----------------------------------------------------------------------------

See also

ilpubs.stanford.edu:8090/778/1/2006-13.pdf -----------------------------------------------------------------------------

Linear Supertypes
Error, Clusterer, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. KMeansPPClusterer
  2. Error
  3. Clusterer
  4. AnyRef
  5. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new KMeansPPClusterer(x: MatrixD, k: Int, algo: Algorithm = HARTIGAN, s: Int = 0)

    x

    the vectors/points to be clustered stored as rows of a matrix

    k

    the number of clusters to make

    algo

    the clustering algorithm to use

    s

    the random number stream (to vary the clusters made)

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. val DEBUG: Boolean
    Attributes
    protected
  5. val MAX_ITER: Int
    Attributes
    protected
  6. var _k: Int
    Attributes
    protected
  7. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  8. val cent: MatrixD
    Attributes
    protected
  9. def centroids(): MatrixD

    Return the centroids.

    Return the centroids. Should only be called after cluster ().

    Definition Classes
    KMeansPPClustererClusterer
  10. 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

    Definition Classes
    KMeansPPClustererClusterer
  11. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  12. 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.

    Definition Classes
    KMeansPPClustererClusterer
  13. def clusterHartigan(): Array[Int]

    Cluster the points using a version of the Hartigan-Wong algorithm.

    Cluster the points using a version of the Hartigan-Wong algorithm.

    See also

    www.tqmp.org/RegularArticles/vol09-1/p015/p015.pdf

  14. val clustered: Boolean

    Flag indicating whether the points have already been clusterer

    Flag indicating whether the points have already been clusterer

    Attributes
    protected
    Definition Classes
    Clusterer
  15. val clustr: Array[Int]
    Attributes
    protected
  16. def csize(): VectorI

    Return the sizes of the centroids.

    Return the sizes of the centroids. Should only be called after cluster ().

    Definition Classes
    KMeansPPClustererClusterer
  17. 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

    Definition Classes
    Clusterer
  18. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  19. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  20. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  21. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  22. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  23. def getName(i: Int): String

    Get the name of the i-th cluster.

    Get the name of the i-th cluster.

    Definition Classes
    Clusterer
  24. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  25. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  26. def name_(n: Array[String]): Unit

    Set the names for the clusters.

    Set the names for the clusters.

    n

    the array of names

    Definition Classes
    Clusterer
  27. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  28. final def notify(): Unit
    Definition Classes
    AnyRef
  29. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  30. val pdf: VectorD
    Attributes
    protected
  31. val raniv: PermutedVecI
    Attributes
    protected
  32. val sizes: VectorI
    Attributes
    protected
  33. def sse(): Double

    Compute the sum of squared errors (distance sqaured from centroid for all points)

  34. def sse(c: Int): Double

    Compute the sum of squared errors (distance squared) from all points in cluster 'c' to the cluster's centroid.

    Compute the sum of squared errors (distance squared) from all points in cluster 'c' to the cluster's centroid.

    c

    the current cluster

  35. 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.

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

    Definition Classes
    Clusterer
  36. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  37. def toString(): String
    Definition Classes
    AnyRef → Any
  38. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  39. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  40. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Error

Inherited from Clusterer

Inherited from AnyRef

Inherited from Any

Ungrouped