jsat.clustering.dissimilarity

Interface UpdatableClusterDissimilarity

All Superinterfaces:

ClusterDissimilarity

All Known Implementing Classes:

AverageLinkDissimilarity, CentroidDissimilarity, CompleteLinkDissimilarity, LanceWilliamsDissimilarity, MedianDissimilarity, SingleLinkDissimilarity, WardsDissimilarity

public interface UpdatableClusterDissimilarity
extends ClusterDissimilarity

This interface extends the contract of a ClusterDissimilarity for more efficient computation. This contract indicates that the dissimilarity measure being used can be computed in an online fashion, and that the dissimilarity matrix can be updated to reflect the dissimilarity for a new merged cluster.

Method Summary

Modifier and Type	Method and Description
UpdatableClusterDissimilarity	clone()
double	dissimilarity(int i, int ni, int j, int nj, double[][] distanceMatrix) Provides the notion of dissimilarity between two sets of points, that may not have the same number of points.
double	dissimilarity(int i, int ni, int j, int nj, int k, int nk, double[][] distanceMatrix) Provides the notion of dissimilarity between two sets of points, that may not have the same number of points.

Methods inherited from interface jsat.clustering.dissimilarity.ClusterDissimilarity

dissimilarity, dissimilarity, distance

Method Detail

dissimilarity

double dissimilarity(int i,
                     int ni,
                     int j,
                     int nj,
                     double[][] distanceMatrix)

Provides the notion of dissimilarity between two sets of points, that may not have the same number of points. This is done using a matrix containing all pairwise distance computations between all points. This distance matrix will then be updated at each iteration and merging, leaving empty space in the matrix. The updates will be done by the clustering algorithm. Implementing this interface indicates that this dissimilarity measure can be accurately computed in an updatable manner that is compatible with a Lance–Williams update.

Parameters:

i - the index of cluster i's distance in the original data set

ni - the number of items in the cluster represented by i

j - the index of cluster j's distance in the original data set

nj - the number of items in the cluster represented by j

distanceMatrix - a distance matrix originally created by AbstractClusterDissimilarity.createDistanceMatrix(jsat.DataSet, jsat.clustering.dissimilarity.ClusterDissimilarity)

Returns:

a value >= 0 that describes the dissimilarity of the two clusters. The larger the value, the more different the two clusterings are.

dissimilarity

double dissimilarity(int i,
                     int ni,
                     int j,
                     int nj,
                     int k,
                     int nk,
                     double[][] distanceMatrix)

Provides the notion of dissimilarity between two sets of points, that may not have the same number of points. This is done using a matrix containing all pairwise distance computations between all points. This distance matrix will then be updated at each iteration and merging, leaving empty space in the matrix. The updates will be done by the clustering algorithm. Implementing this interface indicates that this dissimilarity measure can be accurately computed in an updatable manner that is compatible with a Lance–Williams update.
This computes the dissimilarity of the union of clusters i and j, (C_i ∪ C_j), with the cluster k. This method is used by other algorithms to perform an update of the distance matrix in an efficient manner.

Parameters:

i - the index of cluster i's distance in the original data set

ni - the number of items in the cluster represented by i

j - the index of cluster j's distance in the original data set

nj - the number of items in the cluster represented by j

k - the index of cluster k's distance in the original data set

nk - the number of items in the cluster represented by k a distance matrix originally created by AbstractClusterDissimilarity.createDistanceMatrix(jsat.DataSet, jsat.clustering.dissimilarity.ClusterDissimilarity)

Returns:

a value >= 0 that describes the dissimilarity of the union of two clusters with a third cluster. The larger the value, the more different the resulting clusterings are.

clone

UpdatableClusterDissimilarity clone()

Specified by:

clone in interface ClusterDissimilarity