Package | Description |
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de.lmu.ifi.dbs.elki.data.model |
Cluster models classes for various algorithms.
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de.lmu.ifi.dbs.elki.datasource.filter.normalization |
Data normalization.
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de.lmu.ifi.dbs.elki.datasource.filter.normalization.columnwise |
Normalizations operating on columns / variates; where each column is treated independently.
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de.lmu.ifi.dbs.elki.datasource.filter.normalization.instancewise |
Instancewise normalization, where each instance is normalized independently.
|
Modifier and Type | Method and Description |
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LinearEquationSystem |
CorrelationAnalysisSolution.getNormalizedLinearEquationSystem(Normalization<?> normalization)
Returns the linear equation system for printing purposes.
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Modifier and Type | Class and Description |
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class |
AbstractNormalization<V extends NumberVector>
Abstract super class for all normalizations.
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class |
AbstractStreamNormalization<V extends NumberVector>
Abstract super class for all normalizations.
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Modifier and Type | Class and Description |
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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 |
AttributeWiseErfNormalization<V extends NumberVector>
Attribute-wise Normalization using the error function.
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class |
AttributeWiseMADNormalization<V extends NumberVector>
Median Absolute Deviation is used for scaling the data set as follows:
First, the median, and median absolute deviation are computed in each axis.
|
class |
AttributeWiseMeanNormalization<V extends NumberVector>
Normalization designed for data with a meaningful zero: Each
attribute is scaled to have the same mean (but 0 is not changed).
|
class |
AttributeWiseMinMaxNormalization<V extends NumberVector>
Class to perform and undo a normalization on real vectors with respect to
given minimum and maximum in each dimension.
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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).
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Modifier and Type | Class and Description |
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class |
HellingerHistogramNormalization<V extends NumberVector>
Normalize histograms by scaling them to L1 norm 1, then taking the square
root in each attribute.
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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).
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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.
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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.
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Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.