jsat.clustering.dissimilarity

Interface ClusterDissimilarity

All Known Subinterfaces:

UpdatableClusterDissimilarity

All Known Implementing Classes:

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

public interface ClusterDissimilarity

This interface provides the basic contract for measuring the dissimilarity between two clusters, and intended for use in Hierarchical Agglomerative Clustering.

Author:

Edward Raff

Method Summary

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

Method Detail

distance

double distance(DataPoint a,
                DataPoint b)

Provides the notion of distance, or dissimilarity, between two data points

Parameters:

a - the first data point

b - the second data point

Returns:

a value >= 0 that is a measure of the difference between the two points. The closer to zero, the more similar the points are.

dissimilarity

double dissimilarity(List<DataPoint> a,
                     List<DataPoint> b)

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

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

double dissimilarity(Set<Integer> a,
                     Set<Integer> b,
                     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.

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.

clone

ClusterDissimilarity clone()