Provides the generic functions and the methods for the set operations
union(), intersect(), setequal(), setdiff() and
is.element() on sets of associations (e.g., rules, itemsets) and
itemMatrix.
Usage
# S3 method for class 'itemMatrix'
union(x, y, ...)
# S3 method for class 'associations'
union(x, y, ...)
# S4 method for class 'associations'
union(x, y, ...)
# S4 method for class 'itemMatrix'
union(x, y, ...)
# S3 method for class 'itemMatrix'
intersect(x, y, ...)
# S3 method for class 'associations'
intersect(x, y, ...)
# S4 method for class 'associations'
intersect(x, y, ...)
# S4 method for class 'itemMatrix'
intersect(x, y, ...)
# S3 method for class 'itemMatrix'
setequal(x, y, ...)
# S3 method for class 'associations'
setequal(x, y, ...)
# S4 method for class 'associations'
setequal(x, y, ...)
# S4 method for class 'itemMatrix'
setequal(x, y, ...)
# S3 method for class 'itemMatrix'
setdiff(x, y, ...)
# S3 method for class 'associations'
setdiff(x, y, ...)
# S4 method for class 'associations'
setdiff(x, y, ...)
# S4 method for class 'itemMatrix'
setdiff(x, y, ...)
# S3 method for class 'itemMatrix'
is.element(el, set, ...)
# S3 method for class 'associations'
is.element(el, set, ...)
# S4 method for class 'associations'
is.element(el, set, ...)
# S4 method for class 'itemMatrix'
is.element(el, set, ...)Value
union(), intersect(), setequal() and setdiff()
return an object of the same class as x and y.
is.element() returns a logic vector of length el indicating for
each element if it is included in set.
Details
Technical note: All S4 methods for set operations are defined for the class name
"ANY" in the signature, so they should work for all S4 classes for
which the following methods are available: match(), length() and
unique().
See also
Other associations functions:
abbreviate(),
associations-class,
c,
duplicated(),
extract,
inspect(),
is.closed(),
is.generator(),
is.maximal(),
is.redundant(),
is.significant(),
is.superset(),
itemsets-class,
match(),
rules-class,
sample(),
size(),
sort(),
unique()
Other itemMatrix and transactions functions:
abbreviate(),
c,
crossTable(),
duplicated(),
extract,
hierarchy,
image,
inspect(),
is.superset(),
itemFrequency(),
itemFrequencyPlot(),
itemMatrix-class,
itemwiseSetOps,
match(),
merge(),
random.transactions(),
sample(),
size(),
supportingTransactions(),
tidLists-class,
transactions-class,
unique()
Examples
data("Adult")
## mine some rules
r <- apriori(Adult)
#> 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: 4884
#>
#> set item appearances ...[0 item(s)] done [0.00s].
#> set transactions ...[115 item(s), 48842 transaction(s)] done [0.03s].
#> sorting and recoding items ... [31 item(s)] done [0.01s].
#> creating transaction tree ... done [0.02s].
#> checking subsets of size 1 2 3 4 5 6 7 8 9 done [0.09s].
#> writing ... [6137 rule(s)] done [0.00s].
#> creating S4 object ... done [0.01s].
## take 2 subsets
r1 <- r[1:10]
r2 <- r[6:15]
union(r1, r2)
#> set of 15 rules
intersect(r1, r2)
#> set of 5 rules
setequal(r1, r2)
#> [1] FALSE