Apriori

java.lang.Object
- weka.associations.AbstractAssociator
- - weka.associations.Apriori

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, Associator, CARuleMiner, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler

public class Apriori
extends AbstractAssociator
implements OptionHandler, CARuleMiner, TechnicalInformationHandler

Class implementing an Apriori-type algorithm. Iteratively reduces the minimum support until it finds the required number of rules with the given minimum confidence.
The algorithm has an option to mine class association rules. It is adapted as explained in the second reference.

For more information see:

R. Agrawal, R. Srikant: Fast Algorithms for Mining Association Rules in Large Databases. In: 20th International Conference on Very Large Data Bases, 478-499, 1994.

Bing Liu, Wynne Hsu, Yiming Ma: Integrating Classification and Association Rule Mining. In: Fourth International Conference on Knowledge Discovery and Data Mining, 80-86, 1998.

BibTeX:

 @inproceedings{Agrawal1994,
    author = {R. Agrawal and R. Srikant},
    booktitle = {20th International Conference on Very Large Data Bases},
    pages = {478-499},
    publisher = {Morgan Kaufmann, Los Altos, CA},
    title = {Fast Algorithms for Mining Association Rules in Large Databases},
    year = {1994}
 }
 
 @inproceedings{Liu1998,
    author = {Bing Liu and Wynne Hsu and Yiming Ma},
    booktitle = {Fourth International Conference on Knowledge Discovery and Data Mining},
    pages = {80-86},
    publisher = {AAAI Press},
    title = {Integrating Classification and Association Rule Mining},
    year = {1998}
 }

Valid options are:

 -N <required number of rules output>
  The required number of rules. (default = 10)

 -T <0=confidence | 1=lift | 2=leverage | 3=Conviction>
  The metric type by which to rank rules. (default = confidence)

 -C <minimum metric score of a rule>
  The minimum confidence of a rule. (default = 0.9)

 -D <delta for minimum support>
  The delta by which the minimum support is decreased in
  each iteration. (default = 0.05)

 -U <upper bound for minimum support>
  Upper bound for minimum support. (default = 1.0)

 -M <lower bound for minimum support>
  The lower bound for the minimum support. (default = 0.1)

 -S <significance level>
  If used, rules are tested for significance at
  the given level. Slower. (default = no significance testing)

 -I
  If set the itemsets found are also output. (default = no)

 -R
  Remove columns that contain all missing values (default = no)

 -V
  Report progress iteratively. (default = no)

 -A
  If set class association rules are mined. (default = no)

 -c <the class index>
  The class index. (default = last)

Version:: $Revision: 9096 $
Author:: Eibe Frank (eibe@cs.waikato.ac.nz), Mark Hall (mhall@cs.waikato.ac.nz), Stefan Mutter (mutter@cs.waikato.ac.nz)
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type Field and Description

static Tag[] TAGS_SELECTION
Metric types.

Fields
Modifier and Type	Field and Description
`static Tag[]`	`TAGS_SELECTION` Metric types.

Constructor Summary

Constructors
Constructor and Description

Apriori()
Constructor that allows to sets default values for the minimum confidence and the maximum number of rules the minimum confidence.

Constructors
Constructor and Description
`Apriori()` Constructor that allows to sets default values for the minimum confidence and the maximum number of rules the minimum confidence.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildAssociations(Instances instances)` Method that generates all large itemsets with a minimum support, and from these all association rules with a minimum confidence.
`java.lang.String`	`carTipText()` Returns the tip text for this property
`java.lang.String`	`classIndexTipText()` Returns the tip text for this property
`java.lang.String`	`deltaTipText()` Returns the tip text for this property
`FastVector[]`	`getAllTheRules()` returns all the rules
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`boolean`	`getCar()` Gets whether class association ruels are mined
`int`	`getClassIndex()` Gets the class index
`double`	`getDelta()` Get the value of delta.
`Instances`	`getInstancesNoClass()` Gets the instances without the class atrribute.
`Instances`	`getInstancesOnlyClass()` Gets only the class attribute of the instances.
`double`	`getLowerBoundMinSupport()` Get the value of lowerBoundMinSupport.
`SelectedTag`	`getMetricType()` Get the metric type
`double`	`getMinMetric()` Get the value of minConfidence.
`int`	`getNumRules()` Get the value of numRules.
`java.lang.String[]`	`getOptions()` Gets the current settings of the Apriori object.
`boolean`	`getOutputItemSets()` Gets whether itemsets are output as well
`boolean`	`getRemoveAllMissingCols()` Returns whether columns containing all missing values are to be removed
`java.lang.String`	`getRevision()` Returns the revision string.
`double`	`getSignificanceLevel()` Get the value of significanceLevel.
`TechnicalInformation`	`getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`double`	`getUpperBoundMinSupport()` Get the value of upperBoundMinSupport.
`boolean`	`getVerbose()` Gets whether algorithm is run in verbose mode
`java.lang.String`	`globalInfo()` Returns a string describing this associator
`java.util.Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`java.lang.String`	`lowerBoundMinSupportTipText()` Returns the tip text for this property
`static void`	`main(java.lang.String[] args)` Main method.
`java.lang.String`	`metricString()` Returns the metric string for the chosen metric type
`java.lang.String`	`metricTypeTipText()` Returns the tip text for this property
`FastVector[]`	`mineCARs(Instances data)` Method that mines all class association rules with minimum support and with a minimum confidence.
`java.lang.String`	`minMetricTipText()` Returns the tip text for this property
`java.lang.String`	`numRulesTipText()` Returns the tip text for this property
`java.lang.String`	`outputItemSetsTipText()` Returns the tip text for this property
`java.lang.String`	`removeAllMissingColsTipText()` Returns the tip text for this property
`void`	`resetOptions()` Resets the options to the default values.
`void`	`setCar(boolean flag)` Sets class association rule mining
`void`	`setClassIndex(int index)` Sets the class index
`void`	`setDelta(double v)` Set the value of delta.
`void`	`setLowerBoundMinSupport(double v)` Set the value of lowerBoundMinSupport.
`void`	`setMetricType(SelectedTag d)` Set the metric type for ranking rules
`void`	`setMinMetric(double v)` Set the value of minConfidence.
`void`	`setNumRules(int v)` Set the value of numRules.
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setOutputItemSets(boolean flag)` Sets whether itemsets are output as well
`void`	`setRemoveAllMissingCols(boolean r)` Remove columns containing all missing values.
`void`	`setSignificanceLevel(double v)` Set the value of significanceLevel.
`void`	`setUpperBoundMinSupport(double v)` Set the value of upperBoundMinSupport.
`void`	`setVerbose(boolean flag)` Sets verbose mode
`java.lang.String`	`significanceLevelTipText()` Returns the tip text for this property
`java.lang.String`	`toString()` Outputs the size of all the generated sets of itemsets and the rules.
`java.lang.String`	`upperBoundMinSupportTipText()` Returns the tip text for this property
`java.lang.String`	`verboseTipText()` Returns the tip text for this property

Methods inherited from class weka.associations.AbstractAssociator
forName, makeCopies, makeCopy

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

- Field Detail
  - TAGS_SELECTION
```
public static final Tag[] TAGS_SELECTION
```
    Metric types.
- Constructor Detail
  - Apriori
```
public Apriori()
```
    Constructor that allows to sets default values for the minimum confidence and the maximum number of rules the minimum confidence.
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing this associator
    
    Returns:
    
    a description of the evaluator suitable for displaying in the explorer/experimenter gui
  - getTechnicalInformation
```
public TechnicalInformation getTechnicalInformation()
```
    Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
    
    Specified by:
    
    getTechnicalInformation in interface TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - resetOptions
```
public void resetOptions()
```
    Resets the options to the default values.
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface Associator
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class AbstractAssociator
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - buildAssociations
```
public void buildAssociations(Instances instances)
                       throws java.lang.Exception
```
    Method that generates all large itemsets with a minimum support, and from these all association rules with a minimum confidence.
    
    Specified by:
    
    buildAssociations in interface Associator
    
    Parameters:
    
    instances - the instances to be used for generating the associations
    
    Throws:
    
    java.lang.Exception - if rules can't be built successfully
  - mineCARs
```
public FastVector[] mineCARs(Instances data)
                      throws java.lang.Exception
```
    Method that mines all class association rules with minimum support and with a minimum confidence.
    
    Specified by:
    
    mineCARs in interface CARuleMiner
    
    Parameters:
    
    data - the instances for which class association rules should be mined
    
    Returns:
    
    an sorted array of FastVector (confidence depended) containing the rules and metric information
    
    Throws:
    
    java.lang.Exception - if rules can't be built successfully
  - getInstancesNoClass
```
public Instances getInstancesNoClass()
```
    Gets the instances without the class atrribute.
    
    Specified by:
    
    getInstancesNoClass in interface CARuleMiner
    
    Returns:
    
    the instances without the class attribute.
  - getInstancesOnlyClass
```
public Instances getInstancesOnlyClass()
```
    Gets only the class attribute of the instances.
    
    Specified by:
    
    getInstancesOnlyClass in interface CARuleMiner
    
    Returns:
    
    the class attribute of all instances.
  - listOptions
```
public java.util.Enumeration listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    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:
```
 -N <required number of rules output>
  The required number of rules. (default = 10)
 
```
```
 -T <0=confidence | 1=lift | 2=leverage | 3=Conviction>
  The metric type by which to rank rules. (default = confidence)
 
```
```
 -C <minimum metric score of a rule>
  The minimum confidence of a rule. (default = 0.9)
 
```
```
 -D <delta for minimum support>
  The delta by which the minimum support is decreased in
  each iteration. (default = 0.05)
 
```
```
 -U <upper bound for minimum support>
  Upper bound for minimum support. (default = 1.0)
 
```
```
 -M <lower bound for minimum support>
  The lower bound for the minimum support. (default = 0.1)
 
```
```
 -S <significance level>
  If used, rules are tested for significance at
  the given level. Slower. (default = no significance testing)
 
```
```
 -I
  If set the itemsets found are also output. (default = no)
 
```
```
 -R
  Remove columns that contain all missing values (default = no)
 
```
```
 -V
  Report progress iteratively. (default = no)
 
```
```
 -A
  If set class association rules are mined. (default = no)
 
```
```
 -c <the class index>
  The class index. (default = last)
 
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    java.lang.Exception - if an option is not supported
  - getOptions
```
public java.lang.String[] getOptions()
```
    Gets the current settings of the Apriori object.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - toString
```
public java.lang.String toString()
```
    Outputs the size of all the generated sets of itemsets and the rules.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a string representation of the model
  - metricString
```
public java.lang.String metricString()
```
    Returns the metric string for the chosen metric type
    
    Specified by:
    
    metricString in interface CARuleMiner
    
    Returns:
    
    a string describing the used metric for the interestingness of a class association rule
  - removeAllMissingColsTipText
```
public java.lang.String removeAllMissingColsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setRemoveAllMissingCols
```
public void setRemoveAllMissingCols(boolean r)
```
    Remove columns containing all missing values.
    
    Parameters:
    
    r - true if cols are to be removed.
  - getRemoveAllMissingCols
```
public boolean getRemoveAllMissingCols()
```
    Returns whether columns containing all missing values are to be removed
    
    Returns:
    
    true if columns are to be removed.
  - upperBoundMinSupportTipText
```
public java.lang.String upperBoundMinSupportTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getUpperBoundMinSupport
```
public double getUpperBoundMinSupport()
```
    Get the value of upperBoundMinSupport.
    
    Returns:
    
    Value of upperBoundMinSupport.
  - setUpperBoundMinSupport
```
public void setUpperBoundMinSupport(double v)
```
    Set the value of upperBoundMinSupport.
    
    Parameters:
    
    v - Value to assign to upperBoundMinSupport.
  - setClassIndex
```
public void setClassIndex(int index)
```
    Sets the class index
    
    Specified by:
    
    setClassIndex in interface CARuleMiner
    
    Parameters:
    
    index - the class index
  - getClassIndex
```
public int getClassIndex()
```
    Gets the class index
    
    Returns:
    
    the index of the class attribute
  - classIndexTipText
```
public java.lang.String classIndexTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setCar
```
public void setCar(boolean flag)
```
    Sets class association rule mining
    
    Parameters:
    
    flag - if class association rules are mined, false otherwise
  - getCar
```
public boolean getCar()
```
    Gets whether class association ruels are mined
    
    Returns:
    
    true if class association rules are mined, false otherwise
  - carTipText
```
public java.lang.String carTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - lowerBoundMinSupportTipText
```
public java.lang.String lowerBoundMinSupportTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getLowerBoundMinSupport
```
public double getLowerBoundMinSupport()
```
    Get the value of lowerBoundMinSupport.
    
    Returns:
    
    Value of lowerBoundMinSupport.
  - setLowerBoundMinSupport
```
public void setLowerBoundMinSupport(double v)
```
    Set the value of lowerBoundMinSupport.
    
    Parameters:
    
    v - Value to assign to lowerBoundMinSupport.
  - getMetricType
```
public SelectedTag getMetricType()
```
    Get the metric type
    
    Returns:
    
    the type of metric to use for ranking rules
  - metricTypeTipText
```
public java.lang.String metricTypeTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setMetricType
```
public void setMetricType(SelectedTag d)
```
    Set the metric type for ranking rules
    
    Parameters:
    
    d - the type of metric
  - minMetricTipText
```
public java.lang.String minMetricTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getMinMetric
```
public double getMinMetric()
```
    Get the value of minConfidence.
    
    Returns:
    
    Value of minConfidence.
  - setMinMetric
```
public void setMinMetric(double v)
```
    Set the value of minConfidence.
    
    Parameters:
    
    v - Value to assign to minConfidence.
  - numRulesTipText
```
public java.lang.String numRulesTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNumRules
```
public int getNumRules()
```
    Get the value of numRules.
    
    Returns:
    
    Value of numRules.
  - setNumRules
```
public void setNumRules(int v)
```
    Set the value of numRules.
    
    Parameters:
    
    v - Value to assign to numRules.
  - deltaTipText
```
public java.lang.String deltaTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getDelta
```
public double getDelta()
```
    Get the value of delta.
    
    Returns:
    
    Value of delta.
  - setDelta
```
public void setDelta(double v)
```
    Set the value of delta.
    
    Parameters:
    
    v - Value to assign to delta.
  - significanceLevelTipText
```
public java.lang.String significanceLevelTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getSignificanceLevel
```
public double getSignificanceLevel()
```
    Get the value of significanceLevel.
    
    Returns:
    
    Value of significanceLevel.
  - setSignificanceLevel
```
public void setSignificanceLevel(double v)
```
    Set the value of significanceLevel.
    
    Parameters:
    
    v - Value to assign to significanceLevel.
  - setOutputItemSets
```
public void setOutputItemSets(boolean flag)
```
    Sets whether itemsets are output as well
    
    Parameters:
    
    flag - true if itemsets are to be output as well
  - getOutputItemSets
```
public boolean getOutputItemSets()
```
    Gets whether itemsets are output as well
    
    Returns:
    
    true if itemsets are output as well
  - outputItemSetsTipText
```
public java.lang.String outputItemSetsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setVerbose
```
public void setVerbose(boolean flag)
```
    Sets verbose mode
    
    Parameters:
    
    flag - true if algorithm should be run in verbose mode
  - getVerbose
```
public boolean getVerbose()
```
    Gets whether algorithm is run in verbose mode
    
    Returns:
    
    true if algorithm is run in verbose mode
  - verboseTipText
```
public java.lang.String verboseTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getAllTheRules
```
public FastVector[] getAllTheRules()
```
    returns all the rules
    
    Returns:
    
    all the rules
    
    See Also:
    
    m_allTheRules
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractAssociator
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] args)
```
    Main method.
    
    Parameters:
    
    args - the commandline options

Class Apriori

Field Summary

Constructor Summary

Method Summary

Methods inherited from class weka.associations.AbstractAssociator

Methods inherited from class java.lang.Object

Field Detail

TAGS_SELECTION

Constructor Detail

Apriori

Method Detail

globalInfo

getTechnicalInformation

resetOptions

getCapabilities

buildAssociations

mineCARs

getInstancesNoClass

getInstancesOnlyClass

listOptions

setOptions

getOptions

toString

metricString

removeAllMissingColsTipText

setRemoveAllMissingCols

getRemoveAllMissingCols

upperBoundMinSupportTipText

getUpperBoundMinSupport

setUpperBoundMinSupport

setClassIndex

getClassIndex

classIndexTipText

setCar

getCar

carTipText

lowerBoundMinSupportTipText

getLowerBoundMinSupport

setLowerBoundMinSupport

getMetricType

metricTypeTipText

setMetricType

minMetricTipText

getMinMetric

setMinMetric

numRulesTipText

getNumRules

setNumRules

deltaTipText

getDelta

setDelta

significanceLevelTipText

getSignificanceLevel

setSignificanceLevel

setOutputItemSets

getOutputItemSets

outputItemSetsTipText

setVerbose

getVerbose

verboseTipText

getAllTheRules

getRevision

main