Uses of Interface
weka.filters.UnsupervisedFilter

Packages that use UnsupervisedFilter

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

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.
class	AddExpression Applys a mathematical expression involving attributes and numeric constants to a dataset.
class	AddNoise Introduces noise data a random subsample of the dataset by changing a given attribute (attribute must be nominal) Valid options are: -C col Index of the attribute to be changed.
class	ClusterMembership A filter that uses a clusterer to obtain cluster membership probabilites for each input instance and outputs them as new instances.
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. eg: Original attribute values 0.1, 0.2, 0.3, 0.1, 0.3 New attribute values 0.1, 0.1, 0.1, -0.2, -0.2 The range of attributes used is taken in numeric order.
class	MakeIndicator Creates a new dataset with a boolean attribute replacing a nominal attribute.
class	MergeTwoValues Merges two values of a nominal attribute.
class	NominalToBinary Converts all nominal attributes into binary numeric attributes.
class	Normalize Normalizes all numeric values in the given dataset.
class	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.
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.
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 (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).
class	Remove An instance filter that deletes a range of attributes from the dataset.
class	RemoveType A filter that removes attributes of a given type.
class	RemoveUseless This filter removes attributes that do not vary at all or that vary too much.
class	ReplaceMissingValues Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
class	Standardize Standardizes all numeric attributes in the given dataset to have zero mean and unit variance.
class	StringToNominal Converts a string attribute (i.e. unspecified number of values) to nominal (i.e. set number of values).
class	StringToWordVector Converts String attributes into a set of 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 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

class	NonSparseToSparse A filter that converts all incoming instances into sparse format.
class	Randomize This filter 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	RemoveMisclassified A filter that removes instances which are incorrectly classified.
class	RemovePercentage This filter removes a given percentage of a dataset.
class	RemoveRange This filter takes a dataset and removes a subset of it.
class	RemoveWithValues Filters instances according to the value of an attribute.
class	Resample Produces a random subsample of a dataset.
class	SparseToNonSparse A filter that converts all incoming sparse instances into non-sparse format.