arules (version 1.3-0)

hierarchy: Aggregate Items Into Hierarchical Item Groups

Description

Provides the generic functions and the S4 methods for aggregating items in rules and itemsets into hierarchical groups.

Often an item hierarchy is available for datasets used for association rule mining. For example in a supermarket dataset items like "bread" and "beagle" might belong to the item group (category) "baked goods". The aggregate methods replaces items in transactions, itemsets or rules by item groups as specified by the user.

Usage

## S3 method for class 'itemMatrix':
aggregate(x, by)
## S3 method for class 'itemsets':
aggregate(x, by)
## S3 method for class 'rules':
aggregate(x, by)

Arguments

x

an transactions, itemsets or rules object.

by

name of a field available in itemInfo or a vector of character strings (factor) of the same length as items in x by which should be aggregated. Items receiving the same label in by will be aggregated into a single,

Value

• This method returns an object of the same class as x encoded with a number of items equal to the number of unique values in by. Note that for associations (itemsets and rules) the number of associations in the returned set will most likely be reduced since several associations might map to the same aggregated association and aggregate returns a unique set. If several associations map to a single aggregated association then the quality measures of one of the original associations is randomly chosen.

Details

Transactions can store item hierarchies as additional columns in the itemInfo data.frame ("labels" is reserved for the item labels). These item hierarchies can be used for aggregation. If rules are aggregated and the aggregation would lead to the same aggregated item in the lhs and in the rhs then the item is removed from the lhs. Rules or itemsets which are not unique after the aggregation are also removed.

Examples

data("Groceries")
Groceries
  
## Groceries contains a hierarchy stored in itemInfo
head(itemInfo(Groceries))

## aggregate by level2
Groceries_level2 <- aggregate(Groceries, by = "level2")
Groceries_level2
head(itemInfo(Groceries_level2))
inspect(head(Groceries_level2))

## create lables manually (organize items by the first letter)
mylevels <- toupper(substr(itemLabels(Groceries), 1, 1))
head(mylevels)

Groceries_alpha <- aggregate(Groceries, by = mylevels)
Groceries_alpha
inspect(head(Groceries_alpha))

## aggregate rules (note: you could also directly mine rules from aggregated
## transactions)
rules <- apriori(Groceries, parameter=list(supp=0.005, conf=0.5))
rules
inspect(rules[1])

rules_level2 <- aggregate(rules, by = "level2")
inspect(rules_level2[1])

## interest measures need to be recalculated from aggregated transactions
quality(rules_level2) <- interestMeasure(rules_level2, 
  measure = c("support", "confidence", "lift"), transactions = Groceries_level2)
inspect(rules_level2[1])