JCBAPruning

java.lang.Object
- weka.associations.classification.PruneCAR
- - weka.associations.classification.CrTree
  - - weka.associations.classification.JCBAPruning

All Implemented Interfaces:

java.io.Serializable, weka.core.OptionHandler

Direct Known Subclasses:

PrecedencePruning
```
public class JCBAPruning
extends CrTree
implements weka.core.OptionHandler, java.io.Serializable
```
Class implemting the pruning step of the CBA algorithm using a CrTree. The Tree Structure is described in: W. Li, J. Han, J.Pei: CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules. In ICDM'01:369-376,2001. The CBA algorithm is described in: B. Liu, W. Hsu, Y. Ma: Integrating Classification and Association Rule Mining. In KDD'98:80-86,1998. Valid options are:
-C the confidence value
The confidence value for the optional pessimistic-error-rate-based pruning step (default: 0.25).
-N
If set no pessimistic-error-rate-based pruning is performed.

Version:

$Revision: 10209 $

Author:

Stefan Mutter

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

JCBAPruning()
Constructor

Constructors
Constructor and Description
`JCBAPruning()` Constructor

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`static double`	`addErrs(double N, double e, float CF)` Computes estimated pessimistic error rate for given total number of instances and error using normal approximation to binomial distribution (and continuity correction).
`int`	`calculateDefaultClass(weka.core.Instances RemainingClassInstances)` Calculates the default class as the majority class in the instances
`double`	`calculateError(weka.core.FastVector premise, weka.core.FastVector consequence)` Calculates the pessimistic error rate of a rule
`int`	`getClassValue(int index)` Gets the consequence (the class label) of a rule as an integer value.
`java.lang.String[]`	`getOptions()` Gets the current settings of the Apriori object.
`weka.core.FastVector`	`getPrecedenceList()` Gets the sorted list (according to the interestingness measure) of all rules.
`int`	`getStopIndex()` Gets the number of rules that should be used for classification
`void`	`insertContent(CrNode node, weka.core.FastVector input)` Insert the consequence and the interestingness measures of a rule and builds up the precedence information that allows a ranking according to the interestingness measures
`java.util.Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`int`	`numClassRules()` The number of rules in the tree.
`int`	`numMinedRules()` Gets the number of rules after the mining process that tried to get inserted into the CrTree
`int`	`numPrunedRules()` Gets the number of rules left after the (optional) pessimistic-error-rate-based pruning step.
`void`	`optPruning(boolean flag, float value)` Sets optional pruning on or off and its confidence value
`void`	`preprocess(java.util.ArrayList<java.lang.Object> premises, java.util.ArrayList<java.lang.Object> consequences, java.util.ArrayList<java.lang.Object> confidences)` The preprocessing step before a rule is inserted into a CrTree.
`void`	`prune()` Method that implements the obligatory pruning step
`void`	`pruneBeforeInsertion(weka.core.FastVector premise, weka.core.FastVector consequence)` Performs the (optional) pessimistic-error.rate-based pruning step.
`void`	`resetOptions()` Resets the options to the default values.
`void`	`setDefaultClass(int i)` Sets the default class in each step during obligatory pruning.
`void`	`setInstances(weka.core.Instances instances)` Sets the instances (including the class attribute)
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`java.lang.String`	`toString(java.lang.String metricString)` Returns a string description of the rule set stored in the tree

Methods inherited from class weka.associations.classification.CrTree
deleteContent, deleteNode, getAssociateList, getDefaultClass, getRoot, insertNode, isEmpty, makeEmpty, pathToString, prunedRules, pruningCriterions, removeAtChild, removeAtList, removeAtSibling, reportSubtreeCount, rulePremise, search, setDefaultClass, setInstancesNoClass, setInstancesOnlyClass, sortItemSet, updateHeight

Methods inherited from class weka.associations.classification.PruneCAR
forName

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Constructor Detail
  - JCBAPruning
```
public JCBAPruning()
```
    Constructor
- Method Detail
  - resetOptions
```
public void resetOptions()
```
    Resets the options to the default values.
  - pruneBeforeInsertion
```
public void pruneBeforeInsertion(weka.core.FastVector premise,
                                 weka.core.FastVector consequence)
```
    Performs the (optional) pessimistic-error.rate-based pruning step. If the rule is not pruned, the method inserts it into the CrTree.
    
    Overrides:
    
    pruneBeforeInsertion in class CrTree
    
    Parameters:
    
    premise - the rule premise
    
    consequence - the consequence and interestingness measures
  - insertContent
```
public void insertContent(CrNode node,
                          weka.core.FastVector input)
```
    Insert the consequence and the interestingness measures of a rule and builds up the precedence information that allows a ranking according to the interestingness measures
    
    Overrides:
    
    insertContent in class CrTree
    
    Parameters:
    
    node - the node in the tree where the consequence should be inserted
    
    input - the consequence
  - calculateError
```
public double calculateError(weka.core.FastVector premise,
                             weka.core.FastVector consequence)
```
    Calculates the pessimistic error rate of a rule
    
    Parameters:
    
    premise - the premise
    
    consequence - the consequence
    
    Returns:
    
    the pessimistic error rate
  - addErrs
```
public static double addErrs(double N,
                             double e,
                             float CF)
```
    Computes estimated pessimistic error rate for given total number of instances and error using normal approximation to binomial distribution (and continuity correction).
    
    Parameters:
    
    N - number of instances
    
    e - observed error
    
    CF - confidence value
    
    Returns:
    
    estimated pessimistic error rate
  - preprocess
```
public void preprocess(java.util.ArrayList<java.lang.Object> premises,
                       java.util.ArrayList<java.lang.Object> consequences,
                       java.util.ArrayList<java.lang.Object> confidences)
                throws java.lang.Exception
```
    The preprocessing step before a rule is inserted into a CrTree. The main purpose is to sort the items according to their frequencies in the datsaset. More frequent items will be found in nodes closer to the root.
    
    Overrides:
    
    preprocess in class CrTree
    
    Parameters:
    
    premises - the premises
    
    consequences - the consequences
    
    confidences - the interestingness measures
    
    Throws:
    
    java.lang.Exception - throws exception if preprocessing is not possible
  - prune
```
public void prune()
```
    Method that implements the obligatory pruning step
    
    Overrides:
    
    prune in class CrTree
  - getStopIndex
```
public int getStopIndex()
```
    Gets the number of rules that should be used for classification
    
    Returns:
    
    index in the sorted list before which the rules are used for classification.
  - getPrecedenceList
```
public weka.core.FastVector getPrecedenceList()
```
    Gets the sorted list (according to the interestingness measure) of all rules. The FastVector contains a pointer to a node containing the consequence and the least frequent item of the premise of a rule.
    
    Returns:
    
    FastVector containing CrNodes.
  - getClassValue
```
public int getClassValue(int index)
```
    Gets the consequence (the class label) of a rule as an integer value.
    
    Parameters:
    
    index - the rank of the rule in the sort order induced by the interestingness measure.
    
    Returns:
    
    the consequence of a rule (that is a class label) as a integer.
  - setInstances
```
public void setInstances(weka.core.Instances instances)
```
    Sets the instances (including the class attribute)
    
    Parameters:
    
    instances - the instances for which class association rules are mined.
  - calculateDefaultClass
```
public int calculateDefaultClass(weka.core.Instances RemainingClassInstances)
```
    Calculates the default class as the majority class in the instances
    
    Parameters:
    
    RemainingClassInstances - the set of instances
    
    Returns:
    
    the default class label
  - setDefaultClass
```
public void setDefaultClass(int i)
```
    Sets the default class in each step during obligatory pruning.
    
    Parameters:
    
    i - -1, if the default class is the majority class in the data the index of the rule in the sort order induced by the interestingness measure, if the default class is set during obligatory pruning.
  - getOptions
```
public java.lang.String[] getOptions()
```
    Gets the current settings of the Apriori object.
    
    Specified by:
    
    getOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class CrTree
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - listOptions
```
public java.util.Enumeration listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class CrTree
    
    Returns:
    
    an enumeration of all the available options.
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses a given list of options. Valid options are:
    -C the confidence value
    The confidence value for the optional pessimistic-error-rate-based pruning step (default: 0.25).
    -N
    If set no pessimistic-error-rate-based pruning is performed.
    
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class CrTree
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    java.lang.Exception - if an option is not supported
  - optPruning
```
public void optPruning(boolean flag,
                       float value)
```
    Sets optional pruning on or off and its confidence value
    
    Parameters:
    
    value - the confidence value
    
    flag - flag indicating whether optional pruning should be on or off
  - toString
```
public java.lang.String toString(java.lang.String metricString)
```
    Returns a string description of the rule set stored in the tree
    
    Overrides:
    
    toString in class CrTree
    
    Parameters:
    
    metricString - the metric used as interestingness measure
    
    Returns:
    
    outputs the stored rule set as a string
  - numMinedRules
```
public int numMinedRules()
```
    Gets the number of rules after the mining process that tried to get inserted into the CrTree
    
    Returns:
    
    the (initial) number of rules after the mining step.
  - numPrunedRules
```
public int numPrunedRules()
```
    Gets the number of rules left after the (optional) pessimistic-error-rate-based pruning step.
    
    Returns:
    
    the number of rules left after the optional pruning step.
  - numClassRules
```
public int numClassRules()
```
    The number of rules in the tree. After the pruning step this number equals the number of rules used for classification.
    
    Returns:
    
    the number of rules stored in the tree.

Class JCBAPruning

Constructor Summary

Method Summary

Methods inherited from class weka.associations.classification.CrTree

Methods inherited from class weka.associations.classification.PruneCAR

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Constructor Detail

JCBAPruning

Method Detail

resetOptions

pruneBeforeInsertion

insertContent

calculateError

addErrs

preprocess

prune

getStopIndex

getPrecedenceList

getClassValue

setInstances

calculateDefaultClass

setDefaultClass

getOptions

listOptions

setOptions

optPruning

toString

numMinedRules

numPrunedRules

numClassRules