Packages

class HierClusterer extends Clusterer with Error

Cluster several vectors/points using hierarchical clustering. Start with each point forming its own cluster and merge clusters until there are only 'k'.

Linear Supertypes
Error, Clusterer, AnyRef, Any
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  1. HierClusterer
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Instance Constructors

  1. new HierClusterer(x: MatrixD, k: Int = 2)

    x

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

    k

    stop when the number of clusters equals k

Value Members

  1. def calcCentroids(): Unit

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

  2. def centroids(): MatrixD

    Return the centroids.

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

    Definition Classes
    HierClusterer → Clusterer
  3. 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
    HierClusterer → Clusterer
  4. def clustDist(setA: Set[VectorD], setB: Set[VectorD]): Double

    Create initial clusters where each point forms its own cluster.

    Create initial clusters where each point forms its own cluster.

    setA

    the first set

    setB

    the second set

  5. def cluster(): Array[Int]

    Iteratively merge clusters until until the number of clusters equals k.

    Iteratively merge clusters until until the number of clusters equals k.

    Definition Classes
    HierClusterer → Clusterer
  6. def csize(): VectorI

    Return the sizes of the centroids.

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

    Definition Classes
    HierClusterer → Clusterer
  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

    Definition Classes
    Clusterer
  8. def finalClusters(): Unit

    For each data point, determine its cluster assignment.

  9. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  10. def getName(i: Int): String

    Get the name of the i-th cluster.

    Get the name of the i-th cluster.

    Definition Classes
    Clusterer
  11. def initClusters(): Unit

    Create initial clusters where each point forms its own cluster.

  12. 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
  13. 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