Uses of Interface
elki.datasource.filter.normalization.Normalization

Packages that use Normalization Package Description elki.data.model Cluster models classes for various algorithms.elki.datasource.filter.normalization.columnwise Normalizations operating on columns / variates; where each column is treated independently.elki.datasource.filter.normalization.instancewise Instancewise normalization, where each instance is normalized independently. 

Uses of Normalization in elki.data.model
Methods in elki.data.model with parameters of type Normalization Modifier and Type Method Description LinearEquationSystem
CorrelationAnalysisSolution. getNormalizedLinearEquationSystem(Normalization<?> normalization)
Returns the linear equation system for printing purposes. 
Uses of Normalization in elki.datasource.filter.normalization.columnwise
Classes in elki.datasource.filter.normalization.columnwise that implement Normalization Modifier and Type Class Description class
AttributeWiseBetaNormalization<V extends NumberVector>
Project the data using a Beta distribution.class
AttributeWiseCDFNormalization<V extends NumberVector>
Class to perform and undo a normalization on real vectors by estimating the distribution of values along each dimension independently, then rescaling objects to the cumulative density function (CDF) value at the original coordinate.class
AttributeWiseMADNormalization<V extends NumberVector>
Median Absolute Deviation is used for scaling the data set as follows:class
AttributeWiseMeanNormalization<V extends NumberVector>
Normalization designed for data with a meaningful zero:
The 0 is retained, and the data is linearly scaled to have a mean of 1, by projection with f(x) = x / mean(X).class
AttributeWiseMinMaxNormalization<V extends NumberVector>
Class to perform and undo a normalization on real vectors with respect to a given minimum and maximum in each dimension.class
AttributeWiseVarianceNormalization<V extends NumberVector>
Class to perform and undo a normalization on real vectors with respect to given mean and standard deviation in each dimension.class
InverseDocumentFrequencyNormalization<V extends SparseNumberVector>
Normalization for text frequency (TF) vectors, using the inverse document frequency (IDF). 
Uses of Normalization in elki.datasource.filter.normalization.instancewise
Classes in elki.datasource.filter.normalization.instancewise that implement Normalization Modifier and Type Class Description class
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 \(1/\sqrt{2}\).class
InstanceLogRankNormalization<V extends NumberVector>
Normalize vectors such that the smallest value of each instance is 0, the largest is 1, but using \( \log_2(1+x) \).class
InstanceMeanVarianceNormalization<V extends NumberVector>
Normalize vectors such that they have zero mean and unit variance.class
InstanceMinMaxNormalization<V extends NumberVector>
Normalize vectors with respect to a given minimum and maximum in each dimension.class
InstanceRankNormalization<V extends NumberVector>
Normalize vectors such that the smallest value of each instance is 0, the largest is 1.class
LengthNormalization<V extends NumberVector>
Class to perform a normalization on vectors to norm 1.class
Log1PlusNormalization<V extends NumberVector>
Normalize the data set by applying \( \frac{\log(1+xb)}{\log 1+b} \) to any value.
