Uses of Class
jsat.clustering.dissimilarity.LanceWilliamsDissimilarity

Packages that use LanceWilliamsDissimilarity

Package	Description
jsat.clustering.dissimilarity
jsat.clustering.hierarchical

Uses of LanceWilliamsDissimilarity in jsat.clustering.dissimilarity

Subclasses of LanceWilliamsDissimilarity in jsat.clustering.dissimilarity

Modifier and Type	Class and Description
class	AverageLinkDissimilarity 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.
class	CentroidDissimilarity Average similarity of all data point pairs between clusters, inter-cluster pairs are ignored.
class	CompleteLinkDissimilarity Measures the dissimilarity of two clusters by returning the value of the maximal dissimilarity of any two pairs of data points where one is from each cluster.
class	MedianDissimilarity Median link dissimilarity, also called WPGMC.
class	SingleLinkDissimilarity Measures the dissimilarity of two clusters by returning the minimum dissimilarity between the two closest data points from the clusters, ie: the minimum distance needed to link the two clusters.
class	WardsDissimilarity An implementation of Ward's method for hierarchical clustering.

Methods in jsat.clustering.dissimilarity that return LanceWilliamsDissimilarity

Modifier and Type	Method and Description
abstract LanceWilliamsDissimilarity	LanceWilliamsDissimilarity.clone()

Constructors in jsat.clustering.dissimilarity with parameters of type LanceWilliamsDissimilarity

Constructor and Description
LanceWilliamsDissimilarity(LanceWilliamsDissimilarity toCopy) Copy constructor

Uses of LanceWilliamsDissimilarity in jsat.clustering.hierarchical

Constructors in jsat.clustering.hierarchical with parameters of type LanceWilliamsDissimilarity

Constructor and Description
NNChainHAC(LanceWilliamsDissimilarity distMeasure) Creates a new NNChainHAC
NNChainHAC(LanceWilliamsDissimilarity distMeasure, DistanceMetric distance) Creates a new NNChain using the given dissimilarity measure and distance metric.