Uses of Package
weka.filters.unsupervised.attribute

Packages that use weka.filters.unsupervised.attribute

Package	Description
weka.classifiers.bayes
weka.classifiers.functions
weka.classifiers.meta
weka.classifiers.mi
weka.classifiers.rules
weka.classifiers.trees
weka.clusterers
weka.datagenerators.classifiers.regression
weka.filters.unsupervised.attribute

Classes in weka.filters.unsupervised.attribute used by weka.classifiers.bayes

Class and Description
ReplaceMissingValues Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.

Classes in weka.filters.unsupervised.attribute used by weka.classifiers.functions

Class and Description
NominalToBinary Converts all nominal attributes into binary numeric attributes.
Normalize Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
ReplaceMissingValues Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.

Classes in weka.filters.unsupervised.attribute used by weka.classifiers.meta

Class and Description
Discretize An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
MakeIndicator A filter that creates a new dataset with a boolean attribute replacing a nominal attribute.
Normalize Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
Remove A filter that removes a range of attributes from the dataset.
RemoveUseless This filter removes attributes that do not vary at all or that vary too much.

Classes in weka.filters.unsupervised.attribute used by weka.classifiers.mi

Class and Description
Discretize An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
MultiInstanceToPropositional Converts the multi-instance dataset into single instance dataset so that the Nominalize, Standardize and other type of filters or transformation can be applied to these data for the further preprocessing. Note: the first attribute of the converted dataset is a nominal attribute and refers to the bagId.
NominalToBinary Converts all nominal attributes into binary numeric attributes.
PropositionalToMultiInstance Converts the propositional instance dataset into multi-instance dataset (with relational attribute).
ReplaceMissingValues Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.

Classes in weka.filters.unsupervised.attribute used by weka.classifiers.rules

Class and Description
Remove A filter that removes a range of attributes from the dataset.

Classes in weka.filters.unsupervised.attribute used by weka.classifiers.trees

Class and Description
ReplaceMissingValues Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.

Classes in weka.filters.unsupervised.attribute used by weka.clusterers

Class and Description
ReplaceMissingValues Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.

Classes in weka.filters.unsupervised.attribute used by weka.datagenerators.classifiers.regression

Class and Description
AddExpression An instance filter that creates a new attribute by applying a mathematical expression to existing attributes.

Classes in weka.filters.unsupervised.attribute used by weka.filters.unsupervised.attribute

Class and Description
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.
Center Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).
Discretize An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
NominalToBinary Converts all nominal attributes into binary numeric attributes.
PotentialClassIgnorer This filter should be extended by other unsupervised attribute filters to allow processing of the class attribute if that's required.
Remove A filter that removes a range of attributes from the dataset.
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 (apart from the class attribute, if set).
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.