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

Packages that use Normalization

Package	Description
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.datasource.filter.normalization.columnwise

Classes in elki.datasource.filter.normalization.columnwise that implement Normalization

Modifier and Type	Class	Description
class	AttributeWiseBetaNormalization<V extends elki.data.NumberVector>	Project the data using a Beta distribution.
class	AttributeWiseCDFNormalization<V extends elki.data.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 elki.data.NumberVector>	Median Absolute Deviation is used for scaling the data set as follows:
class	AttributeWiseMeanNormalization<V extends elki.data.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 elki.data.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 elki.data.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 elki.data.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 elki.data.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 elki.data.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 elki.data.NumberVector>	Normalize vectors such that they have zero mean and unit variance.
class	InstanceMinMaxNormalization<V extends elki.data.NumberVector>	Normalize vectors with respect to a given minimum and maximum in each dimension.
class	InstanceRankNormalization<V extends elki.data.NumberVector>	Normalize vectors such that the smallest value of each instance is 0, the largest is 1.
class	LengthNormalization<V extends elki.data.NumberVector>	Class to perform a normalization on vectors to norm 1.
class	Log1PlusNormalization<V extends elki.data.NumberVector>	Normalize the data set by applying \( \frac{\log(1+|x|b)}{\log 1+b} \) to any value.