Package edu.udo.cs.miningmart.operator

Class Summary
Apriori  
AssignAverageValue This operator takes the average of the values in the column that has missing values, and replaces missing values with this average.
AssignDefault This operator replaces the missing values with a default value which is specified with the parameter DefaultValue.
AssignMedianValue This operator replaces missing values by the median value of the column.
AssignModalValue This operator replaces missing values with the modal value of that column.
AssignStochasticValue This operator uses statistical information about the distribution of the values in the target attribute to randomly choose replacements for the missing values such that the distribution is not expected to change.
Binarify This operator creates new binary attributes that realise a boolean flag indicating for each value of the target attribute whether it occurs in that row/entity.
ComputeSVMError This class computes an error for the SupportVectorMachine.
ConceptOperator This abstract class is the superclass for all operators whose output is a concept.
CreatePrimaryKey This operator is able to convert a view with no key and maybe multiple occurences of tuples into a view with a single primary key attribute.
DateToNumeric  
DeleteRecordsWithMissingValues  
Discretization The class Discretization is abstract class for the operators of type Discretization
EvaluateAdvantageOfTFIDFTransformation  
EvaluateResults This class is the superclass for evaluation operators.
ExecutableOperator Abstract superclass of all executable operators in MiningMart.
ExponentialMovingFunction  
FeatureConstruction This class is the superclass for all operators whose output is a BaseAttribute.
FeatureConstructionWithTFIDF  
FeatureSelection This class is the super class for all Feature Selection operators.
FeatureSelectionByAttributes This operator chooses all Features that are present in TheOutputConcept.
FeatureSelectionWithSVM This operator uses the SVM algorithm mySVM/db, which estimates the generalisation performance of an SVM on different feature subsets, to choose the best feature subset.
GenericFeatureConstruction This operator constructs a new feature by using SQL code (provided by the parameter sql_string) for the column definition for the new feature.
JoinByKey This operator joins several concepts using their specified keys.
LinearScaling  
LogScaling  
ManualDiscretization The class ManualDiscretization is abstract class for the operators of type ManualDiscretization
Mapping The class Mapping is abstract class for the operators of type Mapping
MappingWithDefaultValue The class MappingWithDefaultValue maps values for which no mapping has been specified into a constant value It implements abstract method getDefault declared in Mapping.
Materialize  
MissingValues The abstract superclass of all operators that replace a missing value with a new value.
MissingValuesWithRegressionSVM The class MissingValuesWithRegressionSVM implements the abstract methods createFunction and defineOutputDatatype (from class AttributeOperator).
ModelApplier This class is the abstract super-class for model-applying operators.
MultipleCSOperator This abstract class is the superclass for all operators that create more than one ColumnSet for the output concept.
MultiRelationalFeatureConstruction  
NumericalIntervalManualDiscretization The class NumericalIntervalManualDiscretization implements method generateSQL creating virtual column definition for discretization of numerical intervals according to given discretization specification.
PartialMapping The class PartialMapping maps values for which no mapping has been specified into a target attribute value.
Pivotize This operator denormalizes or flattens out certain attributes, which is called Pivotizing.
PrepareForYale This operator creates a YALE experiment file (XML) to ease the combination of MiningMart preprocessing and YALE learning.
RemoveDuplicates Creates a view on the input table/view that contains no two equal tuples (rows).
Repeat  
ReversePivotize This operator denormalizes or flattens out certain attributes, which is called Pivotizing.
RowSelection  
RowSelectionByDeleteMissingValues  
RowSelectionByQuery  
RowSelectionByRandomSampling This operator randomly selects rows with a probability that is computed such that roughly as many rows are selected as are given in the parameter HowMany.
RowSelectionByUnbiasing  
Scaling  
Segmentation This is the abstract super class for all Segmentation operators.
SegmentationByPartitioning This operator segments an input concept randomly.
SegmentationStratified This operator segments an input concept according to the different values of a specified attribute, such that each segment contains the rows where this attribute has the same value.
SignalToSymbolProcessing  
SimpleMovingFunction  
SingleCSOperator This abstract class is the superclass for all operators that create a single ColumnSet for the output concept.
SpecifiedStatistics Creates a table in the business data schema with the statistics values in them.
SupportVectorMachineForClassification This operator uses mySVM to realize a Support Vector Machine for classification.
SupportVectorMachineForRegression This operator uses mySVM to realize a Support Vector Machine for regression.
SVMforDataMining Abstract superclass for the two operators SupportVectorMachineForClassification and SupportVectorMachineForRegression
TimeIntervalManualDiscretization The class TimeIntervalManualDiscretization implements method generateSQL creating virtual column definition for discretization of numerical intervals according to given discretization specification.
TimeOperator Abstract super class for all time operators creating a new table by calling a stored procedure of the database.
TupleWiseModelApplier  
Union This operator provides a UNION of Concepts as part of SQL at the relational level.
UnionByKey UnionByKey operator takes as an input concepts and selected features from these conpcepts.
Unsegment This class realizes an operator for grouping segments of a Concept together.Applications of the operator SegmetationStratified usually result in several Columnsets for a single Concept.
WeightedMovingFunction  
Windowing  
YaleModelApplier This operator applies a model written by YALE to a set of examples.
 



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