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 and 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 attributes values.
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	KernelFilter Converts the given set of predictor variables 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 expression Valid options are:
class	MergeInfrequentNominalValues Merges all values of the specified nominal attribute that are sufficiently infrequent.
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 (and string) attribute values.
class	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. 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 (i.e.
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	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	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 attributes representing word occurrence (depending on the tokenizer) 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.

Uses of UnsupervisedFilter in weka.filters.unsupervised.instance

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

Modifier and Type	Class and 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.