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. def centroids(): MatrixD

    Return the centroids.

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

    Definition Classes
    KMeansPPClustererClusterer
  2. 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
  3. 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
  4. 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

  5. def csize(): VectorI

    Return the sizes of the centroids.

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

    Definition Classes
    KMeansPPClustererClusterer
  6. 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
  7. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  8. def getName(i: Int): String

    Get the name of the i-th cluster.

    Get the name of the i-th cluster.

    Definition Classes
    Clusterer
  9. 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
  10. def sse(): Double

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

  11. 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

  12. 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