arules (version 1.7-6)

is.superset: Find Super and Subsets

Description

Provides the generic functions is.subset() and is.superset(), and the methods for finding super or subsets in associations and itemMatrix objects.

Usage

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)

Value

returns a logical matrix or a sparse 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.

Arguments

x, y

associations or itemMatrix objects. If y = NULL, the super or subset structure within set x is calculated.

proper

a logical indicating if all or just proper super or subsets.

sparse

a logical indicating if a sparse 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.

Author

Michael Hahsler and Ian Johnson

Details

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.

See Also

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(), crossTable(), c(), duplicated(), extract, hierarchy, image(), inspect(), itemFrequencyPlot(), itemFrequency(), itemMatrix-class, match(), merge(), random.transactions(), sample(), sets, size(), supportingTransactions(), tidLists-class, transactions-class, unique()

Examples

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)