jsat.clustering.dissimilarity

Class AverageLinkDissimilarity

java.lang.Object

jsat.clustering.dissimilarity.AbstractClusterDissimilarity

jsat.clustering.dissimilarity.DistanceMetricDissimilarity

jsat.clustering.dissimilarity.LanceWilliamsDissimilarity

jsat.clustering.dissimilarity.AverageLinkDissimilarity

All Implemented Interfaces:

ClusterDissimilarity, UpdatableClusterDissimilarity

public class AverageLinkDissimilarity
extends LanceWilliamsDissimilarity
implements UpdatableClusterDissimilarity

Also known as Group-Average Agglomerative Clustering (GAAC) and UPGMA, this measure computer the dissimilarity by summing the distances between all possible data point pairs in the union of the clusters.

Constructor Summary

Constructors

Constructor and Description
AverageLinkDissimilarity() Creates a new AverageLinkDissimilarity using the EuclideanDistance
AverageLinkDissimilarity(DistanceMetric dm) Creates a new AverageLinkDissimilarity

Method Summary

Modifier and Type	Method and Description
AverageLinkDissimilarity	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.
double	dissimilarity(java.util.List<DataPoint> a, java.util.List<DataPoint> b) Provides the notion of dissimilarity between two sets of points, that may not have the same number of points.
double	dissimilarity(java.util.Set<java.lang.Integer> a, java.util.Set<java.lang.Integer> b, double[][] distanceMatrix) Provides the notion of dissimilarity between two sets of points, that may not have the same number of points.

Methods inherited from class jsat.clustering.dissimilarity.LanceWilliamsDissimilarity

dissimilarity

Methods inherited from class jsat.clustering.dissimilarity.DistanceMetricDissimilarity

distance

Methods inherited from class jsat.clustering.dissimilarity.AbstractClusterDissimilarity

createDistanceMatrix, getDistance, setDistance

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.clustering.dissimilarity.ClusterDissimilarity

distance

Constructor Detail

AverageLinkDissimilarity

public AverageLinkDissimilarity()

Creates a new AverageLinkDissimilarity using the EuclideanDistance

AverageLinkDissimilarity

public AverageLinkDissimilarity(DistanceMetric dm)

Creates a new AverageLinkDissimilarity

Parameters:

dm - the distance measure to use on individual points

Method Detail

clone

public AverageLinkDissimilarity clone()

Specified by:

clone in interface ClusterDissimilarity

Specified by:

clone in interface UpdatableClusterDissimilarity

Specified by:

clone in class LanceWilliamsDissimilarity

dissimilarity

public double dissimilarity(java.util.List<DataPoint> a,
                            java.util.List<DataPoint> b)

Description copied from interface: ClusterDissimilarity

Provides the notion of dissimilarity between two sets of points, that may not have the same number of points.

Specified by:

dissimilarity in interface ClusterDissimilarity

Overrides:

dissimilarity in class LanceWilliamsDissimilarity

Parameters:

a - the first cluster of points

b - the second cluster of points

Returns:

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

dissimilarity

public double dissimilarity(java.util.Set<java.lang.Integer> a,
                            java.util.Set<java.lang.Integer> b,
                            double[][] distanceMatrix)

Description copied from interface: ClusterDissimilarity

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.

Specified by:

dissimilarity in interface ClusterDissimilarity

Overrides:

dissimilarity in class LanceWilliamsDissimilarity

Parameters:

a - the first set of indices of the original data set that are in a cluster, which map to distanceMatrix

b - the second set of indices of the original data set that are in a cluster, which map to distanceMatrix

distanceMatrix - the upper triangual distance matrix as 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

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

Description copied from interface: UpdatableClusterDissimilarity

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.

Specified by:

dissimilarity in interface UpdatableClusterDissimilarity

Overrides:

dissimilarity in class LanceWilliamsDissimilarity

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

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

Description copied from interface: UpdatableClusterDissimilarity

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.

Specified by:

dissimilarity in interface UpdatableClusterDissimilarity

Overrides:

dissimilarity in class LanceWilliamsDissimilarity

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.