Package weka.filters.unsupervised.attribute

Class Summary

AbstractTimeSeries	An abstract instance filter that assumes instances form time-series data and performs some merging of attribute values in the current instance with attribute attribute values of some previous (or future) instance.
Add	An instance filter that adds a new attribute to the dataset.
AddCluster	A filter that adds a new nominal attribute representing the cluster assigned to each instance by the specified clustering algorithm.
AddExpression	Applys a mathematical expression involving attributes and numeric constants to a dataset.
AddNoise	Introduces noise data a random subsample of the dataset by changing a given attribute (attribute must be nominal) Valid options are:
ClusterMembership	A filter that uses a clusterer to obtain cluster membership probabilites for each input instance and outputs them as new instances.
Copy	An instance filter that copies a range of attributes in the dataset.
Discretize	An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
FirstOrder	This instance filter takes a range of N numeric attributes and replaces them with N-1 numeric attributes, the values of which are the difference between consecutive attribute values from the original instance.
MakeIndicator	Creates a new dataset with a boolean attribute replacing a nominal attribute.
MergeTwoValues	Merges two values of a nominal attribute.
NominalToBinary	Converts all nominal attributes into binary numeric attributes.
Normalize	Normalizes all numeric values in the given dataset.
NumericToBinary	Converts all numeric attributes into binary attributes (apart from the class attribute): if the value of the numeric attribute is exactly zero, the value of the new attribute will be zero.
NumericTransform	Transforms numeric attributes using a given transformation method.
Obfuscate	A simple instance filter that renames the relation, all attribute names and all nominal (and string) attribute values.
PKIDiscretize	Discretizes numeric attributes using equal frequency binning where the number of bins is equal to the square root of the number of non-missing values.
PotentialClassIgnorer	This filter should be extended by other unsupervised attribute filters to allow processing of the class attribute if that's required.
RandomProjection	Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length (It will reduce the number of attributes in the data while preserving much of its variation like PCA, but at a much less computational cost).
Remove	An instance filter that deletes a range of attributes from the dataset.
RemoveType	A filter that removes attributes of a given type.
RemoveUseless	This filter removes attributes that do not vary at all or that vary too much.
ReplaceMissingValues	Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
Standardize	Standardizes all numeric attributes in the given dataset to have zero mean and unit variance.
StringToNominal	Converts a string attribute (i.e.
StringToWordVector	Converts String attributes into a set of attributes representing word occurrence information from the text contained in the strings.
SwapValues	Swaps two values of a nominal attribute.
TimeSeriesDelta	An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the difference between the current value and the equivalent attribute attribute value of some previous (or future) instance.
TimeSeriesTranslate	An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the equivalent attribute attribute values of some previous (or future) instance.