Package smile.clustering.linkage

Class Linkage

java.lang.Object
smile.clustering.linkage.Linkage
Direct Known Subclasses:
CompleteLinkage, SingleLinkage, UPGMALinkage, UPGMCLinkage, WardLinkage, WPGMALinkage, WPGMCLinkage
public abstract class Linkage extends Object
A measure of dissimilarity between clusters (i.e. sets of observations).

References

  1. Anil K. Jain, Richard C. Dubes. Algorithms for clustering data. 1988.
See Also:

  • Constructor Summary

    Constructors
    Constructor
    Description
    Linkage(double[][] proximity)
    Constructor.
    Linkage(int size, float[] proximity)
    Constructor.

  • Method Summary

    Modifier and Type
    Method
    Description
    float
    d(int i, int j)
    Returns the distance/dissimilarity between two clusters/objects, which are indexed by integers.
    abstract void
    merge(int i, int j)
    Merges two clusters into one and update the proximity matrix.
    static float[]
    proximity(double[][] data)
    Computes the proximity matrix (linearized in column major) based on Euclidean distance.
    static <T> float[]
    proximity(T[] data, Distance<T> distance)
    Computes the proximity matrix (linearized in column major).
    int
    size()
    Returns the proximity matrix size.

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

  • Constructor Details

    • Linkage

      public Linkage(double[][] proximity)
      Constructor.
      Parameters:
      proximity - the proximity matrix. Only the lower half will be referred.

    • Linkage

      public Linkage(int size, float[] proximity)
      Constructor.
      Parameters:
      size - the data size.
      proximity - the column-wise linearized proximity matrix that stores only the lower half. The length of proximity should be size * (size+1) / 2. To save space, Linkage will use this argument directly without copy. The elements may be modified.

  • Method Details

    • size

      public int size()
      Returns the proximity matrix size.
      Returns:
      the proximity matrix size.

    • d

      public float d(int i, int j)
      Returns the distance/dissimilarity between two clusters/objects, which are indexed by integers.
      Parameters:
      i - the row index of proximity matrix.
      j - the column index of proximity matrix.
      Returns:
      the distance/dissimilarity.

    • merge

      public abstract void merge(int i, int j)
      Merges two clusters into one and update the proximity matrix.
      Parameters:
      i - cluster id.
      j - cluster id.

    • proximity

      public static float[] proximity(double[][] data)
      Computes the proximity matrix (linearized in column major) based on Euclidean distance.
      Parameters:
      data - the data points.
      Returns:
      the linearized proximity matrix based on Eulidean distance.

    • proximity

      public static <T> float[] proximity(T[] data, Distance<T> distance)
      Computes the proximity matrix (linearized in column major).
      Type Parameters:
      T - the data type of points.
      Parameters:
      data - the data points.
      distance - the distance function.
      Returns:
      the linearized proximity matrix.