weka.associations
Class Apriori

java.lang.Object
  |
  +--weka.associations.Associator
        |
        +--weka.associations.Apriori

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable

public class Apriori

extends Associator

implements OptionHandler

Class implementing an Apriori-type algorithm. Iteratively reduces the minimum support until it finds the required number of rules with the given minimum confidence.

Reference: R. Agrawal, R. Srikant (1994). Fast algorithms for mining association rules in large databases . Proc International Conference on Very Large Databases, pp. 478-499. Santiage, Chile: Morgan Kaufmann, Los Altos, CA.

Valid options are:

-N required number of rules
The required number of rules (default: 10).

-T type of metric by which to sort rules
0 = confidence | 1 = lift | 2 = leverage | 3 = Conviction.

-C minimum confidence 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
The upper bound for minimum support. Don't explicitly look for rules with more than this level of support.

-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).

-R
If set then columns that contain all missing values are removed from the data. -I
If set the itemsets found are also output (default = no).

Version:

$Revision: 1.15 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Mark Hall (mhall@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

static Tag[]

TAGS_SELECTION

Constructor Summary

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

Method Summary

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	deltaTipText() Returns the tip text for this property
double	getDelta() Get the value of delta.
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	getRemoveAllMissingCols() Returns whether columns containing all missing values are to be removed
double	getSignificanceLevel() Get the value of significanceLevel.
double	getUpperBoundMinSupport() Get the value of upperBoundMinSupport.
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[] options) Main method for testing this class.
java.lang.String	metricTypeTipText() Returns the tip text for this property
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	removeAllMissingColsTipText() Returns the tip text for this property
void	resetOptions() Resets the options to the default values.
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	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.
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

Methods inherited from class weka.associations.Associator

forName, makeCopies

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

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

resetOptions

public void resetOptions()

Resets the options to the default values.

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 class Associator

Parameters:

instances - the instances to be used for generating the associations

Throws:

java.lang.Exception - if rules can't be built successfully

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
The required number of rules (default: 10).

-T type of metric by which to sort rules
0 = confidence | 1 = lift | 2 = leverage | 3 = Conviction.

-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
The upper bound for minimum support. Don't explicitly look for rules with more than this level of support.

-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).

-V
If set then progress is reported iteratively during execution.

-R
If set then columns that contain all missing values are removed from the data.

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

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.

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.

main

public static void main(java.lang.String[] options)

Main method for testing this class.