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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 | An instance filter that creates a new attribute by applying a mathematical expression to existing attributes.
AddID | An instance filter that adds an ID attribute to the dataset.
AddNoise | An instance filter that changes a percentage of a given attributes values.
AddValues | Adds the labels from the given list to an attribute if they are missing.
Center | Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).
ChangeDateFormat | Changes the date format used by a date attribute.
ClassAssigner | Filter that can set and unset the class index.
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).
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.
InterquartileRange | A filter for detecting outliers and extreme values based on interquartile ranges.
KernelFilter | Converts the given set of predictor variables into a kernel matrix.
MakeIndicator | A filter that creates a new dataset with a boolean attribute replacing a nominal attribute.
MathExpression | Modify numeric attributes according to a given expression Valid options are:
MergeTwoValues | Merges two values of a nominal attribute into one value.
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.
NominalToString | Converts a nominal attribute (i.e.
Normalize | Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
NumericCleaner | A filter that 'cleanses' the numeric data from values that are too small, too big or very close to a certain value (e.g., 0) and sets these values to a pre-defined default.
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.
NumericToNominal | A filter for turning numeric attributes into nominal ones.
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.
PartitionedMultiFilter | A filter that applies filters on subsets of attributes and assembles the output into a new dataset.
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.
PotentialClassIgnorer | This filter should be extended by other unsupervised attribute filters to allow processing of the class attribute if that's required.
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.
PropositionalToMultiInstance | Converts the propositional instance dataset into multi-instance dataset (with relational attribute).
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.
RandomSubset | Chooses a random subset of attributes, either an absolute number or a percentage.
RELAGGS | A propositionalization filter inspired by the RELAGGS algorithm. It processes all relational attributes that fall into the user defined range (all others are skipped, i.e., not added to the output).
Remove | A filter that removes a range of attributes from the dataset.
RemoveType | Removes attributes of a given type.
RemoveUseless | This filter removes attributes that do not vary at all or that vary too much.
Reorder | A filter that generates output with a new order of the attributes.
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).
StringToNominal | Converts a string attribute (i.e.
StringToWordVector | Converts String attributes into a set of attributes representing word occurrence (depending on the tokenizer) 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.
Wavelet | A filter for wavelet transformation. For more information see: Wikipedia (2004).

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