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## 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:
ChangeDateFormat | Changes the date format used by a date attribute.
ClusterMembership | A filter that uses a clusterer to obtain cluster membership values 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 values of some previous (or future) instance.

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