weka.associations.classification

Class LabeledItemSet

java.lang.Object

weka.associations.ItemSet

weka.associations.classification.LabeledItemSet

All Implemented Interfaces:

java.io.Serializable, weka.core.RevisionHandler

public class LabeledItemSet
extends weka.associations.ItemSet
implements java.io.Serializable

Class for storing a set of items together with a class label. 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 the methods used for item sets in class association rule mining. Because every item set knows its class label the training set can be splitted up virtually.

Version:

$Revision: 8108 $

Author:

Stefan Mutter (mutter@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
LabeledItemSet(int totalTrans, int classLabel) Constructor

Method Summary

Modifier and Type	Method and Description
static weka.core.FastVector	deleteItemSets(weka.core.FastVector itemSets, int minSupport, int maxSupport) Deletes all item sets that don't have minimum support and have more than maximum support
static weka.core.Instances	divide(weka.core.Instances instances, boolean invert) Splits the class attribute away.
boolean	equalCondset(java.lang.Object itemSet) Compares two item sets
boolean	equals(java.lang.Object itemSet) Tests if two item sets are equal.
weka.core.FastVector[]	generateRules(double minConfidence, boolean noPrune) Generates rules out of item sets
static java.util.Hashtable	getHashtable(weka.core.FastVector itemSets, int initialSize) Return a hashtable filled with the given item sets.
static weka.core.FastVector	mergeAllItemSets(weka.core.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 weka.core.FastVector	pruneItemSets(weka.core.FastVector toPrune, java.util.Hashtable kMinusOne) Prunes a set of (k)-item sets using the given (k-1)-item sets.
static weka.core.FastVector	singletons(weka.core.Instances instancesNoClass, weka.core.Instances classes) Converts the header info of the given set of instances into a set of item sets (singletons).
int	support() Outputs the support for an item set.
void	upDateCounter(weka.core.Instance instanceNoClass, weka.core.Instance instanceClass) Updates counter of item set with respect to given transaction.
static void	upDateCounters(weka.core.FastVector itemSets, weka.core.Instances instancesNoClass, weka.core.Instances instancesClass) Updates counter of a specific item set

Methods inherited from class weka.associations.ItemSet

containedBy, containedByTreatZeroAsMissing, counter, deleteItemSets, getHashtable, getItems, getRevision, getTotalTransactions, hashCode, itemAt, items, mergeAllItemSets, pruneItemSets, pruneRules, setCounter, setItem, setItemAt, singletons, toString, toString, upDateCounter, upDateCounters, upDateCountersTreatZeroAsMissing, updateCounterTreatZeroAsMissing

Methods inherited from class java.lang.Object

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

Constructor Detail

LabeledItemSet

public LabeledItemSet(int totalTrans,
                      int classLabel)

Constructor

Parameters:

totalTrans - the total number of transactions

classLabel - the class lebel

Method Detail

deleteItemSets

public static weka.core.FastVector deleteItemSets(weka.core.FastVector itemSets,
                                                  int minSupport,
                                                  int maxSupport)

Deletes all item sets that don't have minimum support and have more than maximum support

Parameters:

maxSupport - the maximum support

itemSets - the set of item sets to be pruned

minSupport - the minimum number of transactions to be covered

Returns:

the reduced set of item sets

equals

public final boolean equals(java.lang.Object itemSet)

Tests if two item sets are equal.

Overrides:

equals in class weka.associations.ItemSet

Parameters:

itemSet - another item set

Returns:

true if this item set contains the same items as the given one

equalCondset

public final boolean equalCondset(java.lang.Object itemSet)

Compares two item sets

Parameters:

itemSet - an item set

Returns:

true if the the item sets are equal, false otherwise

getHashtable

public static java.util.Hashtable getHashtable(weka.core.FastVector itemSets,
                                               int initialSize)

Return a hashtable filled with the given item sets.

Parameters:

itemSets - the set of item sets to be used for filling the hash table

initialSize - the initial size of the hashtable

Returns:

the generated hashtable

mergeAllItemSets

public static weka.core.FastVector mergeAllItemSets(weka.core.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:

totalTrans - the total number of transactions

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

size - the value of (k-1)

Returns:

the generated (k)-item sets

divide

public static weka.core.Instances divide(weka.core.Instances instances,
                                         boolean invert)
                                  throws java.lang.Exception

Splits the class attribute away. Depending on the invert flag, the instances without class attribute or only the class attribute of all instances is returned

Parameters:

instances - the instances

invert - flag; if true only the class attribute remains, otherweise the class attribute is the only attribute that is deleted.

Returns:

Instances without the class attribute or instances with only the class attribute

Throws:

java.lang.Exception - exception if instances cannot be splitted

singletons

public static weka.core.FastVector singletons(weka.core.Instances instancesNoClass,
                                              weka.core.Instances classes)
                                       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. Each item set knows its class label.

Parameters:

instancesNoClass - instances without the class attribute

classes - the values of the class attribute sorted according to instances

Returns:

a set of item sets, each containing a single item

Throws:

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

pruneItemSets

public static weka.core.FastVector pruneItemSets(weka.core.FastVector toPrune,
                                                 java.util.Hashtable kMinusOne)

Prunes a set of (k)-item sets using the given (k-1)-item sets.

Parameters:

toPrune - the set of (k)-item sets to be pruned

kMinusOne - the (k-1)-item sets to be used for pruning

Returns:

the pruned set of item sets

support

public final int support()

Outputs the support for an item set.

Overrides:

support in class weka.associations.ItemSet

Returns:

the support

upDateCounter

public final void upDateCounter(weka.core.Instance instanceNoClass,
                                weka.core.Instance instanceClass)

Updates counter of item set with respect to given transaction.

Parameters:

instanceNoClass - instances without the class attribute

instanceClass - the values of the class attribute sorted according to instances

upDateCounters

public static void upDateCounters(weka.core.FastVector itemSets,
                                  weka.core.Instances instancesNoClass,
                                  weka.core.Instances instancesClass)

Updates counter of a specific item set

Parameters:

itemSets - an item sets

instancesNoClass - instances without the class attribute

instancesClass - the values of the class attribute sorted according to instances

generateRules

public final weka.core.FastVector[] generateRules(double minConfidence,
                                                  boolean noPrune)

Generates rules out of item sets

Parameters:

minConfidence - the minimum confidence

noPrune - flag indicating whether the rules are pruned accoridng to the minimum confidence value

Returns:

a set of rules