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 \( \frac{\log(1+|x|b)}{\log 1+b} \) to any value.