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kddml.Operators.Preprocessing.DiscretizationAlgorithms.EQUAL_FREQUENCY_DISCRETIZATION_SETTINGS Class Reference

List of all members.

Public Member Functions

String[] listParameters ()
Boolean isRequiredParameter (String param_name)
Boolean isFixedParameter (String param_name)
String getDefaultParameterValue (String param_name)
KDDMLScalarType getParameterType (String param_name)
void checkCorrelationBetweenParameters (Hashtable< String, KDDMLScalarManager > parameters) throws SettingsException

Detailed Description

Settings class for the Equal Frequency Discretization (EFD) algorithm.
The EFD technique divides the range of a numeric attribute A into k intervals containing the same number of samples. Suppose there are n training instances for which the values of A are known (missing values will be ignored). More in details, the algorithm sorts the observed values and then divides the sorted values into k intervals so that each interval contains (approximately2) the same number of training instances. Thus each interval contains n/k (possibly duplicated) adjacent values. The number of output intervals k and the number of required samples for each interval are mutually exclusive parameters.
When the intervals have been computed, the algorithm replaces each training instance value of A with an interval label. Numeric or nominal labeling are allowed.
A Numeric interval label includes the mean, the median, the minimum or maximum calculated on the values belonging to the interval.
A Nominal interval label includes a list of strings, each containing the labels used to replace each training instance value belonging to the interval. The system guarantees that the number of nominal labels is equal to the number of output intervals k. The mapping between intervals computed by the algorithm and nominal labels starts from the interval containing the lowest values6. As an instance, suppose that the algorithm computes the intervals I1 = [6, 35), I2 = [35, 65) and I3 = [65, 95). Moreover suppose that the nominal labels provided are "young", "adult" and "elder" in that order. For each training input instance, a value v of the discretization attribute is replaced with "young", "adult" and "elder" if v belongs to I1, v belongs I2 and v belongs to I3 respectively. By using the nominal interval labeling, the type of the discretization attribute become enumerated.
Parameters:

Title: KDDML

Description: Knowledge Discovery in Database Environment

Copyright: Copyright (c) 2003-2005

Company: Universita' di Pisa - Dipartimento di Informatica

Author:
Andrea Romei (romei@di.unipi.it)

Sandra Zimei

Version:
2.0.16


Member Function Documentation

String [] kddml.Operators.Preprocessing.DiscretizationAlgorithms.EQUAL_FREQUENCY_DISCRETIZATION_SETTINGS.listParameters  ) 
 

Returns the list of parameters belonging to the algorithm specification.

Returns:
String[]

Boolean kddml.Operators.Preprocessing.DiscretizationAlgorithms.EQUAL_FREQUENCY_DISCRETIZATION_SETTINGS.isRequiredParameter String  param_name  ) 
 

Returns true if a given parameter is required in the algorithm specification. Returns false if the parameter is implied or fixed. Finally, the method returns null if the parameter name do not belong to the list of parameters returned by the method listParameters().

Parameters:
param_name String
Returns:
Boolean

Boolean kddml.Operators.Preprocessing.DiscretizationAlgorithms.EQUAL_FREQUENCY_DISCRETIZATION_SETTINGS.isFixedParameter String  param_name  ) 
 

Returns true if a given parameter is fixed (i.e. constant) in the algorithm specification. Returns false if the parameter is implied or required. Finally, the method returns null if the parameter name do not belong to the list of parameters returned by the method listParameters().

Parameters:
param_name String
Returns:
Boolean

String kddml.Operators.Preprocessing.DiscretizationAlgorithms.EQUAL_FREQUENCY_DISCRETIZATION_SETTINGS.getDefaultParameterValue String  param_name  ) 
 

Returns the default value related to a given input parameter. Returns null if the parameter do not admit a default value. A parameter can be given any legal value as a default. The parameter value is not required on each element in the algorithm specification, but if it is not present, it will appear to be the specified default. If the parameter is implied and it is not included in the element, the method assumes that this is the parameter value.

Parameters:
param_name String
Returns:
String

KDDMLScalarType kddml.Operators.Preprocessing.DiscretizationAlgorithms.EQUAL_FREQUENCY_DISCRETIZATION_SETTINGS.getParameterType String  param_name  ) 
 

Returns the type related to a given input parameter. KDDML supports the parameter types as expressed by the class KDDMLScalarType. Returns null if the parameter name do no belong to the list of parameters returned by the method listParameters().

Parameters:
param_name String
Returns:
KDDMLScalarType

void kddml.Operators.Preprocessing.DiscretizationAlgorithms.EQUAL_FREQUENCY_DISCRETIZATION_SETTINGS.checkCorrelationBetweenParameters Hashtable< String, KDDMLScalarManager parameters  )  throws SettingsException
 

Checks the correctness correlation between algorithm parameters. This method depends on the correlation level between the parameters and it is empty for a specific algorithm if and only if all parameter are independent. As instance, the minimum support and the minimum confidence in the APRIORI algorithm are independent. However, the value of the percentage splitting for a classification algorithm depends on the classification strategy adopted and must be required only if the algorithm uses a splitting technique. The method can incorporate further controls independent on input parameters, e.g. that some particular events are true when the algorithm is applied.

Parameters:
parameters Hashtable the paramters related to the operator with the relative value. The key of the hashtable is the name of the attribute. The value of the hashtable is a KDDMLScalar representing the value of the attribute. Each single attribute value is correct.
Exceptions:
SettingsException if the correlation checking returns an error.


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