Packages

class KMeansClusterer extends Clusterer with Error

The KMeansClusterer class cluster several vectors/points using k-means clustering. Either (1) randomly assign points to 'k' clusters or (2) randomly pick 'k' points as initial centroids (technique (1) to work better and is the primary technique). Iteratively, reassign each point to the cluster containing the closest centroid. Stop when there are no changes to the clusters. -----------------------------------------------------------------------------

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Error, Clusterer, AnyRef, Any
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Instance Constructors

  1. new KMeansClusterer(x: MatrixD, k: Int, s: Int = 0, primary: Boolean = true, remote: Boolean = true, post: Boolean = true)

    x

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

    k

    the number of clusters to make

    s

    the random number stream (to vary the clusters made)

    primary

    true indicates use the primary technique for initiating the clustering

    remote

    whether to take a maximally remote or a randomly selected point

    post

    whether to perform post processing by randomly swapping points to reduce error

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. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. def assign(): Unit

    Randomly assign each vector/point 'x(i)' to a random cluster.

    Randomly assign each vector/point 'x(i)' to a random cluster. Primary technique for initiating the clustering.

  8. def calcCentroids(): Unit

    Calculate the centroids based on current assignment of points to clusters.

  9. val cent: MatrixD
    Attributes
    protected
  10. def centroids(): MatrixD

    Return the centroids.

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

    Definition Classes
    KMeansClusterer → Clusterer
  11. def checkOpt(opt: Double): Boolean

    Check to see if the sum of squared errors is optimum.

    Check to see if the sum of squared errors is optimum.

    opt

    the known (from human/oracle) optimum

  12. def classify(y: VectorD): Int

    Given a new point/vector 'y', determine which cluster it belongs to, i.e., the cluster whose centroid it is closest to.

    Given a new point/vector 'y', determine which cluster it belongs to, i.e., the cluster whose centroid it is closest to.

    y

    the vector to classify

    Definition Classes
    KMeansClusterer → Clusterer
  13. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  14. def cluster(): Array[Int]

    Iteratively recompute clusters until the assignment of points does not change, returning the final cluster assignment vector.

    Iteratively recompute clusters until the assignment of points does not change, returning the final cluster assignment vector.

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

    Return the sizes of the centroids.

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

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

    Get the name of the i-th cluster.

    Get the name of the i-th cluster.

    Definition Classes
    Clusterer
  26. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  27. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  28. val myDist: VectorD
    Attributes
    protected
  29. 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
  30. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  31. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  32. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  33. def pickCentroids(): Unit

    Randomly pick vectors/points to serve as the initial 'k' centroids (cent).

    Randomly pick vectors/points to serve as the initial 'k' centroids (cent). Secondary technique for initiating the clustering.

  34. def reassign(): Boolean

    Reassign each vector/point to the cluster with the closest centroid.

    Reassign each vector/point to the cluster with the closest centroid. Indicate done, if no points changed clusters (for stopping rule).

  35. val sizes: VectorI
    Attributes
    protected
  36. 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

  37. 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
  38. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  39. var tc1: Double
  40. var tc2: Double
  41. def toString(): String
    Definition Classes
    AnyRef → Any
  42. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  43. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  44. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

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Inherited from AnyRef

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