weka.associations
Class AprioriItemSet

java.lang.Object
  weka.associations.ItemSet
      weka.associations.AprioriItemSet

All Implemented Interfaces:

java.io.Serializable

public class AprioriItemSet

extends ItemSet

implements java.io.Serializable

Class for storing a set of items. Item sets are stored in a lexicographic order, which is determined by the header information of the set of instances used for generating the set of items. All methods in this class assume that item sets are stored in lexicographic order. The class provides methods that are used in the Apriori algorithm to construct association rules.

Version:

$Revision: 1.1 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Stefan Mutter (mutter@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

AprioriItemSet(int totalTrans)
Constructor

Method Summary

static double	confidenceForRule(AprioriItemSet premise, AprioriItemSet consequence) Outputs the confidence for a rule.
double	convictionForRule(AprioriItemSet premise, AprioriItemSet consequence, int premiseCount, int consequenceCount) Outputs the conviction for a rule.
FastVector[]	generateRules(double minConfidence, FastVector hashtables, int numItemsInSet) Generates all rules for an item set.
FastVector[]	generateRulesBruteForce(double minMetric, int metricType, FastVector hashtables, int numItemsInSet, int numTransactions, double significanceLevel) Generates all significant rules for an item set.
double	leverageForRule(AprioriItemSet premise, AprioriItemSet consequence, int premiseCount, int consequenceCount) Outputs the leverage for a rule.
double	liftForRule(AprioriItemSet premise, AprioriItemSet consequence, int consequenceCount) Outputs the lift for a rule.
static FastVector	mergeAllItemSets(FastVector itemSets, int size, int totalTrans) Merges all item sets in the set of (k-1)-item sets to create the (k)-item sets and updates the counters.
static FastVector	singletons(Instances instances) Converts the header info of the given set of instances into a set of item sets (singletons).
AprioriItemSet	subtract(AprioriItemSet toSubtract) Subtracts an item set from another one.
java.lang.String	toString(Instances instances) Returns the contents of an item set as a string.

Methods inherited from class weka.associations.ItemSet

containedBy, counter, deleteItemSets, equals, getHashtable, hashCode, itemAt, items, pruneItemSets, pruneRules, setCounter, setItem, setItemAt, support, upDateCounter, upDateCounters

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

AprioriItemSet

public AprioriItemSet(int totalTrans)

Constructor

Parameters:

totalTrans - the total number of transactions in the data

Method Detail

confidenceForRule

public static double confidenceForRule(AprioriItemSet premise,
                                       AprioriItemSet consequence)

Outputs the confidence for a rule.

Parameters:

premise - the premise of the rule

consequence - the consequence of the rule

Returns:

the confidence on the training data

liftForRule

public double liftForRule(AprioriItemSet premise,
                          AprioriItemSet consequence,
                          int consequenceCount)

Outputs the lift for a rule. Lift is defined as:
confidence / prob(consequence)

Parameters:

premise - the premise of the rule

consequence - the consequence of the rule

consequenceCount - how many times the consequence occurs independent of the premise

Returns:

the lift on the training data

leverageForRule

public double leverageForRule(AprioriItemSet premise,
                              AprioriItemSet consequence,
                              int premiseCount,
                              int consequenceCount)

Outputs the leverage for a rule. Leverage is defined as:
prob(premise & consequence) - (prob(premise) * prob(consequence))

Parameters:

premise - the premise of the rule

consequence - the consequence of the rule

premiseCount - how many times the premise occurs independent of the consequent

consequenceCount - how many times the consequence occurs independent of the premise

Returns:

the leverage on the training data

convictionForRule

public double convictionForRule(AprioriItemSet premise,
                                AprioriItemSet consequence,
                                int premiseCount,
                                int consequenceCount)

Outputs the conviction for a rule. Conviction is defined as:
prob(premise) * prob(!consequence) / prob(premise & !consequence)

Parameters:

premise - the premise of the rule

consequence - the consequence of the rule

premiseCount - how many times the premise occurs independent of the consequent

consequenceCount - how many times the consequence occurs independent of the premise

Returns:

the conviction on the training data

generateRules

public FastVector[] generateRules(double minConfidence,
                                  FastVector hashtables,
                                  int numItemsInSet)

Generates all rules for an item set.

Parameters:

minConfidence - the minimum confidence the rules have to have

hashtables - containing all(!) previously generated item sets

numItemsInSet - the size of the item set for which the rules are to be generated

Returns:

all the rules with minimum confidence for the given item set

generateRulesBruteForce

public final FastVector[] generateRulesBruteForce(double minMetric,
                                                  int metricType,
                                                  FastVector hashtables,
                                                  int numItemsInSet,
                                                  int numTransactions,
                                                  double significanceLevel)
                                           throws java.lang.Exception

Generates all significant rules for an item set.

Parameters:

minMetric - the minimum metric (confidence, lift, leverage, improvement) the rules have to have

metricType - (confidence=0, lift, leverage, improvement)

hashtables - containing all(!) previously generated item sets

numItemsInSet - the size of the item set for which the rules are to be generated

Returns:

all the rules with minimum metric for the given item set

Throws:

java.lang.Exception - if something goes wrong

subtract

public final AprioriItemSet subtract(AprioriItemSet toSubtract)

Subtracts an item set from another one.

Parameters:

toSubtract - the item set to be subtracted from this one.

Returns:

an item set that only contains items form this item sets that are not contained by toSubtract

toString

public final java.lang.String toString(Instances instances)

Returns the contents of an item set as a string.

Overrides:

toString in class ItemSet

Parameters:

instances - contains the relevant header information

Returns:

string describing the item set

singletons

public static FastVector singletons(Instances instances)
                             throws java.lang.Exception

Converts the header info of the given set of instances into a set of item sets (singletons). The ordering of values in the header file determines the lexicographic order.

Parameters:

instances - the set of instances whose header info is to be used

Returns:

a set of item sets, each containing a single item

Throws:

java.lang.Exception - if singletons can't be generated successfully

mergeAllItemSets

public static FastVector mergeAllItemSets(FastVector itemSets,
                                          int size,
                                          int totalTrans)

Merges all item sets in the set of (k-1)-item sets to create the (k)-item sets and updates the counters.

Parameters:

itemSets - the set of (k-1)-item sets

size - the value of (k-1)

Returns:

the generated (k)-item sets