Package de.lmu.ifi.dbs.elki.datasource.filter.normalization.instancewise

Instancewise normalization, where each instance is normalized independently.

See: Description

Class Summary

Class	Description
HellingerHistogramNormalization<V extends NumberVector>	Normalize histograms by scaling them to L1 norm 1, then taking the square root in each attribute.
HellingerHistogramNormalization.Parameterizer	Parameterization class.
InstanceLogRankNormalization<V extends NumberVector>	Normalize vectors such that the smallest value of each instance is 0, the largest is 1, but using log_2(1+x).
InstanceLogRankNormalization.Parameterizer	Parameterization class.
InstanceMeanVarianceNormalization<V extends NumberVector>	Normalize vectors such that they have zero mean and unit variance.
InstanceMeanVarianceNormalization.Parameterizer<V extends NumberVector>	Parameterization class.
InstanceMinMaxNormalization<V extends NumberVector>	Normalize vectors such that the smallest attribute is 0, the largest is 1.
InstanceMinMaxNormalization.Parameterizer<V extends NumberVector>	Parameterization class.
InstanceRankNormalization<V extends NumberVector>	Normalize vectors such that the smallest value of each instance is 0, the largest is 1.
InstanceRankNormalization.Parameterizer	Parameterization class.
LengthNormalization<V extends NumberVector>	Class to perform a normalization on vectors to norm 1.
LengthNormalization.Parameterizer<V extends NumberVector>	Parameterization class.
Log1PlusNormalization<V extends NumberVector>	Normalize the data set by applying log(1+|x|*b)/log(b+1) to any value.
Log1PlusNormalization.Parameterizer<V extends NumberVector>	Parameterization class.

Package de.lmu.ifi.dbs.elki.datasource.filter.normalization.instancewise Description

Instancewise normalization, where each instance is normalized independently.