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 unit absolute sum, then taking the square root of the absolute value in each attribute, times the normalization constant \(1/\sqrt{2}\).
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 with respect to a given minimum and maximum in each dimension.
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 \( \frac{\log(1+|x|b)}{\log 1+b} \) 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.