Package  Description 

de.lmu.ifi.dbs.elki.datasource.filter.normalization.instancewise 
Instancewise normalization, where each instance is normalized independently.

Class and Description 

HellingerHistogramNormalization
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 \(1/\sqrt{2}\).

InstanceLogRankNormalization
Normalize vectors such that the smallest value of each instance is 0, the
largest is 1, but using \( \log_2(1+x) \).

InstanceMeanVarianceNormalization
Normalize vectors such that they have zero mean and unit variance.

InstanceMinMaxNormalization
Normalize vectors with respect to a given minimum and maximum in each
dimension.

InstanceRankNormalization
Normalize vectors such that the smallest value of each instance is 0, the
largest is 1.

LengthNormalization
Class to perform a normalization on vectors to norm 1.

Log1PlusNormalization
Normalize the data set by applying \( \frac{\log(1+xb)}{\log 1+b} \) to any
value.

Copyright © 2019 ELKI Development Team. License information.