Provides the generic functions is.subset()
and is.superset()
, and the methods
for finding super or subsets in associations and
itemMatrix objects.
is.superset(x, y = NULL, proper = FALSE, sparse = TRUE, ...)is.subset(x, y = NULL, proper = FALSE, sparse = TRUE, ...)
# S4 method for itemMatrix
is.superset(x, y = NULL, proper = FALSE, sparse = TRUE)
# S4 method for associations
is.superset(x, y = NULL, proper = FALSE, sparse = TRUE)
# S4 method for itemMatrix
is.subset(x, y = NULL, proper = FALSE, sparse = TRUE)
# S4 method for associations
is.subset(x, y = NULL, proper = FALSE, sparse = TRUE)
returns a logical matrix or a sparse Matrix::ngCMatrix
with length(x)
rows and length(y)
columns.
Each logical row vector represents which elements in y
are supersets
(subsets) of the corresponding element in x
. If either x
or
y
have length zero, NULL
is returned instead of a matrix.
associations or itemMatrix objects. If y = NULL
, the super
or subset structure within set x
is calculated.
a logical indicating if all or just proper super or subsets.
a logical indicating if a sparse Matrix::ngCMatrix rather than a dense logical matrix should be returned. Sparse computation requires a significantly smaller amount of memory and is much faster for large sets.
currently unused.
Michael Hahsler and Ian Johnson
Determines for each element in x
which elements in y
are supersets
or subsets. Note that the method can be very slow and memory intensive if
x
and/or y
are very dense (contain many items).
For rules, the union of lhs and rhs is used a the set of items.
Other postprocessing:
is.closed()
,
is.generator()
,
is.maximal()
,
is.redundant()
,
is.significant()
Other associations functions:
abbreviate()
,
associations-class
,
c()
,
duplicated()
,
extract
,
inspect()
,
is.closed()
,
is.generator()
,
is.maximal()
,
is.redundant()
,
is.significant()
,
itemsets-class
,
match()
,
rules-class
,
sample()
,
sets
,
size()
,
sort()
,
unique()
Other itemMatrix and transactions functions:
abbreviate()
,
c()
,
crossTable()
,
duplicated()
,
extract
,
hierarchy
,
image()
,
inspect()
,
itemFrequency()
,
itemFrequencyPlot()
,
itemMatrix-class
,
match()
,
merge()
,
random.transactions()
,
sample()
,
sets
,
size()
,
supportingTransactions()
,
tidLists-class
,
transactions-class
,
unique()
data("Adult")
set <- eclat(Adult, parameter = list(supp = 0.8))
### find the supersets of each itemset in set
is.superset(set, set)
is.superset(set, set, sparse = FALSE)
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