Package | Description |
---|---|
de.lmu.ifi.dbs.elki.datasource.filter.normalization |
Data normalization.
|
de.lmu.ifi.dbs.elki.datasource.filter.normalization.instancewise |
Instancewise normalization, where each instance is normalized independently.
|
de.lmu.ifi.dbs.elki.datasource.filter.transform |
Data space transformations.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractStreamNormalization<V extends NumberVector>
Abstract super class for all normalizations.
|
Modifier and Type | Class and Description |
---|---|
class |
HellingerHistogramNormalization<V extends NumberVector>
Normalize histograms by scaling them to L1 norm 1, then taking the square
root in each attribute.
|
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 such that the smallest attribute is 0, the largest is 1.
|
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 log(1+|x|*b)/log(b+1) to any value.
|
Modifier and Type | Class and Description |
---|---|
class |
HistogramJitterFilter<V extends NumberVector>
Add Jitter, preserving the histogram properties (same sum, nonnegative).
|
class |
NumberVectorFeatureSelectionFilter<V extends NumberVector>
Parser to project the ParsingResult obtained by a suitable base parser onto a
selected subset of attributes.
|
class |
NumberVectorRandomFeatureSelectionFilter<V extends NumberVector>
Parser to project the ParsingResult obtained by a suitable base parser onto a
randomly selected subset of attributes.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.