c

scalation.analytics.clusterer

KMeansClusterer2

class KMeansClusterer2 extends KMeansClusterer

The KMeansClusterer2 class cluster several vectors/points using k-means clustering. Randomly pick 'k' points as initial centroids (secondary technique). Iteratively, reassign each point to the cluster containing the closest centroid. Stop when there are no changes to the clusters.

See also

KMeansClusterer for primary technique. -----------------------------------------------------------------------------

Linear Supertypes
KMeansClusterer, Error, Clusterer, AnyRef, Any
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Inherited
  1. KMeansClusterer2
  2. KMeansClusterer
  3. Error
  4. Clusterer
  5. AnyRef
  6. Any
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Visibility
  1. Public
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Instance Constructors

  1. new KMeansClusterer2(x: MatriD, k: Int, flags: Array[Boolean] = Array (false, false))

    x

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

    k

    the number of clusters to make

    flags

    the flags used to adjust the algorithm

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 MAX_ITER: Int
    Attributes
    protected
    Definition Classes
    KMeansClusterer
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. 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.

    Attributes
    protected
    Definition Classes
    KMeansClusterer
  7. def calcCentroids(x: MatriD, to_c: Array[Int], sz: VectoI, cent: MatriD): Unit

    Calculate the centroids based on current assignment of points to clusters and update the 'cent' matrix that stores the centroids in its rows.

    Calculate the centroids based on current assignment of points to clusters and update the 'cent' matrix that stores the centroids in its rows.

    x

    the data matrix holding the points {x_i = x(i)} in its rows

    to_c

    the cluster assignment array

    sz

    the sizes of the clusters (number of points)

    cent

    the matrix holding the centroids in its rows

    Definition Classes
    Clusterer
  8. val cent: MatrixD
    Attributes
    protected
    Definition Classes
    KMeansClusterer
  9. def centroids: MatriD

    Return the centroids.

    Return the centroids. Should only be called after train.

    Definition Classes
    KMeansClusterer → Clusterer
  10. def checkOpt(x: MatriD, to_c: Array[Int], 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.

    x

    the data matrix holding the points

    to_c

    the cluster assignments

    opt

    the known (from human/oracle) optimum

    Definition Classes
    Clusterer
  11. def classify(z: VectoD): Int

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

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

    z

    the vector to classify

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

    Return the cluster assignment vector.

    Return the cluster assignment vector. Should only be called after train.

    Definition Classes
    KMeansClusterer → Clusterer
  14. def csize: VectoI

    Return the sizes of the centroids.

    Return the sizes of the centroids. Should only be called after train.

    Definition Classes
    KMeansClusterer → Clusterer
  15. def distance(u: VectoD, cn: MatriD, kc_: Int = -1): VectoD

    Compute the distances between vector/point 'u' and the points stored as rows in matrix 'cn'

    Compute the distances between vector/point 'u' and the points stored as rows in matrix 'cn'

    u

    the given vector/point (u = x_i)

    cn

    the matrix holding several centroids

    kc_

    the number of centroids so far

    Definition Classes
    Clusterer
  16. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  17. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  18. def fixEmptyClusters(): Unit

    Fix all empty clusters by taking a point from the largest cluster.

    Fix all empty clusters by taking a point from the largest cluster.

    Attributes
    protected
    Definition Classes
    KMeansClusterer
  19. val flags: Array[Boolean]
    Definition Classes
    KMeansClusterer
  20. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  21. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  22. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  23. val immediate: Boolean
    Attributes
    protected
    Definition Classes
    KMeansClusterer
  24. def initCentroids(): Boolean

    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.

    Definition Classes
    KMeansClusterer2 → Clusterer
  25. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  26. def name(c: Int): String

    Return the name of the 'c'-th cluster.

    Return the name of the 'c'-th cluster.

    c

    the c-th cluster

    Definition Classes
    Clusterer
  27. def name_(nm: Strings): Unit

    Set the names for the clusters.

    Set the names for the clusters.

    nm

    the array of names

    Definition Classes
    Clusterer
  28. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  29. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  30. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  31. val post: Boolean
    Attributes
    protected
    Definition Classes
    KMeansClusterer
  32. var raniv: PermutedVecI
    Attributes
    protected
    Definition Classes
    KMeansClusterer
  33. 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).

    Attributes
    protected
    Definition Classes
    KMeansClusterer
  34. def setStream(s: Int): Unit

    Set the random stream to 's'.

    Set the random stream to 's'. Method must be called in implemeting classes before creating any random generators.

    s

    the new value for the random number stream

    Definition Classes
    Clusterer
  35. def show(l: Int): Unit

    Show the state of the algorithm at iteration 'l'.

    Show the state of the algorithm at iteration 'l'.

    l

    the current iteration

    Definition Classes
    KMeansClusterer
  36. def sse(x: MatriD, c: Int, to_c: Array[Int]): Double

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

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

    x

    the data matrix holding the points

    c

    the current cluster

    to_c

    the cluster assignments

    Definition Classes
    Clusterer
  37. def sse(x: MatriD, to_c: Array[Int]): Double

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

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

    x

    the data matrix holding the points

    to_c

    the cluster assignments

    Definition Classes
    Clusterer
  38. def sst(x: MatriD): Double

    Compute the sum of squares total for all the points from the mean.

    Compute the sum of squares total for all the points from the mean.

    x

    the data matrix holding the points

    Definition Classes
    Clusterer
  39. val stream: Int
    Attributes
    protected
    Definition Classes
    Clusterer
  40. def swap(): Unit

    Try all pairwise swaps and make them if 'sse' improves.

    Try all pairwise swaps and make them if 'sse' improves.

    Attributes
    protected
    Definition Classes
    KMeansClusterer
  41. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  42. val sz: VectorI
    Attributes
    protected
    Definition Classes
    KMeansClusterer
  43. def toString(): String
    Definition Classes
    AnyRef → Any
  44. val to_c: Array[Int]
    Attributes
    protected
    Definition Classes
    KMeansClusterer
  45. def train(): KMeansClusterer

    Iteratively recompute clusters until the assignment of points does not change.

    Iteratively recompute clusters until the assignment of points does not change. Initialize by randomly assigning points to 'k' clusters.

    Definition Classes
    KMeansClusterer → Clusterer
  46. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  47. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  48. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

Inherited from KMeansClusterer

Inherited from Error

Inherited from Clusterer

Inherited from AnyRef

Inherited from Any

Ungrouped