Uses of Interface
weka.filters.UnsupervisedFilter

Packages that use UnsupervisedFilter

Package	Description
weka.filters.unsupervised.attribute
weka.filters.unsupervised.instance

Uses of UnsupervisedFilter in weka.filters.unsupervised.attribute

Classes in weka.filters.unsupervised.attribute that implement UnsupervisedFilter

Modifier and Type	Class	Description
class	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.
class	Add	An instance filter that adds a new attribute to the dataset.
class	AddCluster	A filter that adds a new nominal attribute representing the cluster assigned to each instance by the specified clustering algorithm. Either the clustering algorithm gets built with the first batch of data or one specifies are serialized clusterer model file to use instead.
class	AddExpression	An instance filter that creates a new attribute by applying a mathematical expression to existing attributes.
class	AddID	An instance filter that adds an ID attribute to the dataset.
class	AddNoise	An instance filter that changes a percentage of a given attribute's values.
class	AddUserFields	A filter that adds new attributes with user specified type and constant value.
class	AddValues	Adds the labels from the given list to an attribute if they are missing.
class	Center	Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).
class	ChangeDateFormat	Changes the date format used by a date attribute.
class	ClusterMembership	A filter that uses a density-based clusterer to generate cluster membership values; filtered instances are composed of these values plus the class attribute (if set in the input data).
class	Copy	An instance filter that copies a range of attributes in the dataset.
class	Discretize	An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
class	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.
class	FixedDictionaryStringToWordVector	Converts String attributes into a set of attributes representing word occurrence (depending on the tokenizer) information from the text contained in the strings.
class	KernelFilter	Converts the given set of data into a kernel matrix.
class	MakeIndicator	A filter that creates a new dataset with a Boolean attribute replacing a nominal attribute.
class	MathExpression	Modify numeric attributes according to a given mathematical expression.
class	MergeInfrequentNominalValues	Merges all values of the specified nominal attributes that are insufficiently frequent.
class	MergeManyValues	Merges many values of a nominal attribute into one value.
class	MergeTwoValues	Merges two values of a nominal attribute into one value.
class	NominalToBinary	Converts all nominal attributes into binary numeric attributes.
class	NominalToString	Converts a nominal attribute (i.e.
class	Normalize	Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
class	NumericToBinary	Converts all numeric attributes into binary attributes (apart from the class attribute, if set): if the value of the numeric attribute is exactly zero, the value of the new attribute will be zero.
class	NumericTransform	Transforms numeric attributes using a given transformation method.
class	Obfuscate	A simple instance filter that renames the relation, all attribute names and all nominal attribute values.
class	OrdinalToNumeric	An attribute filter that converts ordinal nominal attributes into numeric ones Valid options are:
class	PKIDiscretize	Discretizes numeric attributes using equal frequency binning and forces the number of bins to be equal to the square root of the number of values of the numeric attribute. For more information, see: Ying Yang, Geoffrey I.
class	PrincipalComponents	Performs a principal components analysis and transformation of the data. Dimensionality reduction is accomplished by choosing enough eigenvectors to account for some percentage of the variance in the original data -- default 0.95 (95%). Based on code of the attribute selection scheme 'PrincipalComponents' by Mark Hall and Gabi Schmidberger.
class	RandomProjection	Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length.
class	Remove	An filter that removes a range of attributes from the dataset.
class	RemoveType	Removes attributes of a given type.
class	RemoveUseless	This filter removes attributes that do not vary at all or that vary too much.
class	RenameNominalValues	Renames the values of nominal attributes.
class	Reorder	A filter that generates output with a new order of the attributes.
class	ReplaceMissingValues	Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
class	ReplaceMissingWithUserConstant	Replaces all missing values for nominal, string, numeric and date attributes in the dataset with user-supplied constant values.
class	ReplaceWithMissingValue	A filter that can be used to introduce missing values in a dataset.
class	Standardize	Standardizes all numeric attributes in the given dataset to have zero mean and unit variance (apart from the class attribute, if set).
class	StringToNominal	Converts a range of string attributes (unspecified number of values) to nominal (set number of values).
class	StringToWordVector	Converts string attributes into a set of numeric attributes representing word occurrence information from the text contained in the strings.
class	SwapValues	Swaps two values of a nominal attribute.
class	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.
class	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.
class	Transpose	Transposes the data: instances become attributes and attributes become instances.

Uses of UnsupervisedFilter in weka.filters.unsupervised.instance

Classes in weka.filters.unsupervised.instance that implement UnsupervisedFilter

Modifier and Type	Class	Description
class	NonSparseToSparse	An instance filter that converts all incoming instances into sparse format.
class	Randomize	Randomly shuffles the order of instances passed through it.
class	RemoveFolds	This filter takes a dataset and outputs a specified fold for cross validation.
class	RemoveFrequentValues	Determines which values (frequent or infrequent ones) of an (nominal) attribute are retained and filters the instances accordingly.
class	RemoveMisclassified	A filter that removes instances which are incorrectly classified.
class	RemovePercentage	A filter that removes a given percentage of a dataset.
class	RemoveRange	A filter that removes a given range of instances of a dataset.
class	RemoveWithValues	Filters instances according to the value of an attribute.
class	Resample	Produces a random subsample of a dataset using either sampling with replacement or without replacement.
class	ReservoirSample	Produces a random subsample of a dataset using the reservoir sampling Algorithm "R" by Vitter.
class	SparseToNonSparse	An instance filter that converts all incoming sparse instances into non-sparse format.