Uses of Class
weka.core.NormalizableDistance

Packages that use NormalizableDistance

Package	Description
weka.clusterers
weka.core

Uses of NormalizableDistance in weka.clusterers

Methods in weka.clusterers with parameters of type NormalizableDistance

Modifier and Type	Method	Description
static Canopy	Canopy.aggregateCanopies(List<Canopy> canopies, double aggregationT1, double aggregationT2, NormalizableDistance finalDistanceFunction, Filter missingValuesReplacer, int finalNumCanopies)	Aggregate the canopies from a list of Canopy clusterers together into one final model.

Uses of NormalizableDistance in weka.core

Subclasses of NormalizableDistance in weka.core

Modifier and Type	Class	Description
class	ChebyshevDistance	Implements the Chebyshev distance.
class	EuclideanDistance	Implementing Euclidean distance (or similarity) function. One object defines not one distance but the data model in which the distances between objects of that data model can be computed. Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low. For more information, see: Wikipedia.
class	ManhattanDistance	Implements the Manhattan distance (or Taxicab geometry).
class	MinkowskiDistance	Implementing Minkowski distance (or similarity) function. One object defines not one distance but the data model in which the distances between objects of that data model can be computed. Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low. For more information, see: Wikipedia.