Provides the methods to combine several associations or transactions objects into a single object.
Usage
# S4 method for class 'itemMatrix'
c(x, ..., recursive = FALSE)
# S4 method for class 'transactions'
c(x, ..., recursive = FALSE)
# S4 method for class 'tidLists'
c(x, ..., recursive = FALSE)
# S4 method for class 'rules'
c(x, ..., recursive = FALSE)
# S4 method for class 'itemsets'
c(x, ..., recursive = FALSE)Details
Combining arules objects is done by combining the rows of itemMatrix objects representing the associations or transactions.
Note that c() can result in duplicates.
Use union() rather than c() to combine several mined
itemsets or rules into a single
set without duplicates.
See also
Other associations functions:
abbreviate(),
associations-class,
duplicated(),
extract,
inspect(),
is.closed(),
is.generator(),
is.maximal(),
is.redundant(),
is.significant(),
is.superset(),
itemsets-class,
match(),
rules-class,
sample(),
sets,
size(),
sort(),
unique()
Other itemMatrix and transactions functions:
abbreviate(),
crossTable(),
duplicated(),
extract,
hierarchy,
image,
inspect(),
is.superset(),
itemFrequency(),
itemFrequencyPlot(),
itemMatrix-class,
itemwiseSetOps,
match(),
merge(),
random.transactions(),
sample(),
sets,
size(),
supportingTransactions(),
tidLists-class,
transactions-class,
unique()
Examples
data("Adult")
## combine transactions
a1 <- Adult[1:10]
a2 <- Adult[101:110]
aComb <- c(a1, a2)
summary(aComb)
#> transactions as itemMatrix in sparse format with
#> 20 rows (elements/itemsets/transactions) and
#> 115 columns (items) and a density of 0.1121739
#>
#> most frequent items:
#> capital-loss=None native-country=United-States
#> 20 18
#> race=White capital-gain=None
#> 17 14
#> hours-per-week=Full-time (Other)
#> 14 175
#>
#> element (itemset/transaction) length distribution:
#> sizes
#> 11 13
#> 1 19
#>
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 11.0 13.0 13.0 12.9 13.0 13.0
#>
#> includes extended item information - examples:
#> labels variables levels
#> 1 age=Young age Young
#> 2 age=Middle-aged age Middle-aged
#> 3 age=Senior age Senior
#>
#> includes extended transaction information - examples:
#> transactionID
#> 1 1
#> 2 2
#> 3 3
## combine rules (can contain the same rule multiple times)
r1 <- apriori(Adult[1:1000])
#> Apriori
#>
#> Parameter specification:
#> confidence minval smax arem aval originalSupport maxtime support minlen
#> 0.8 0.1 1 none FALSE TRUE 5 0.1 1
#> maxlen target ext
#> 10 rules TRUE
#>
#> Algorithmic control:
#> filter tree heap memopt load sort verbose
#> 0.1 TRUE TRUE FALSE TRUE 2 TRUE
#>
#> Absolute minimum support count: 100
#>
#> set item appearances ...[0 item(s)] done [0.00s].
#> set transactions ...[100 item(s), 1000 transaction(s)] done [0.00s].
#> sorting and recoding items ... [31 item(s)] done [0.00s].
#> creating transaction tree ... done [0.00s].
#> checking subsets of size 1 2 3 4 5 6 7 8 done [0.01s].
#> writing ... [8500 rule(s)] done [0.00s].
#> creating S4 object ... done [0.00s].
r2 <- apriori(Adult[1001:2000])
#> Apriori
#>
#> Parameter specification:
#> confidence minval smax arem aval originalSupport maxtime support minlen
#> 0.8 0.1 1 none FALSE TRUE 5 0.1 1
#> maxlen target ext
#> 10 rules TRUE
#>
#> Algorithmic control:
#> filter tree heap memopt load sort verbose
#> 0.1 TRUE TRUE FALSE TRUE 2 TRUE
#>
#> Absolute minimum support count: 100
#>
#> set item appearances ...[0 item(s)] done [0.00s].
#> set transactions ...[101 item(s), 1000 transaction(s)] done [0.00s].
#> sorting and recoding items ... [30 item(s)] done [0.00s].
#> creating transaction tree ... done [0.00s].
#> checking subsets of size 1 2 3 4 5 6 7 8 9 done [0.01s].
#> writing ... [8575 rule(s)] done [0.00s].
#> creating S4 object ... done [0.00s].
rComb <- c(r1, r2)
rComb
#> set of 17075 rules
## union of rules (a set with only unique rules: same as unique(rComb))
rUnion <- union(r1, r2)
rUnion
#> set of 9928 rules