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 class 'itemMatrix'
is.superset(x, y = NULL, proper = FALSE, sparse = TRUE)
# S4 method for class 'associations'
is.superset(x, y = NULL, proper = FALSE, sparse = TRUE)
# S4 method for class 'itemMatrix'
is.subset(x, y = NULL, proper = FALSE, sparse = TRUE)
# S4 method for class 'associations'
is.subset(x, y = NULL, proper = FALSE, sparse = TRUE)Arguments
- x, y
associations or itemMatrix objects. If
y = NULL, the super or subset structure within setxis calculated.- proper
a logical indicating if all or just proper super or subsets.
- sparse
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.
Value
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.
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(),
ruleInduction()
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,
itemwiseSetOps,
match(),
merge(),
random.transactions(),
sample(),
sets,
size(),
supportingTransactions(),
tidLists-class,
transactions-class,
unique()
Examples
data("Adult")
set <- eclat(Adult, parameter = list(supp = 0.8))
#> Eclat
#>
#> parameter specification:
#> tidLists support minlen maxlen target ext
#> FALSE 0.8 1 10 frequent itemsets TRUE
#>
#> algorithmic control:
#> sparse sort verbose
#> 7 -2 TRUE
#>
#> Absolute minimum support count: 39073
#>
#> create itemset ...
#> set transactions ...[115 item(s), 48842 transaction(s)] done [0.03s].
#> sorting and recoding items ... [4 item(s)] done [0.00s].
#> creating bit matrix ... [4 row(s), 48842 column(s)] done [0.00s].
#> writing ... [8 set(s)] done [0.00s].
#> Creating S4 object ... done [0.00s].
### find the supersets of each itemset in set
is.superset(set, set)
#> 8 x 8 sparse Matrix of class "ngCMatrix"
#> {race=White,capital-loss=None}
#> {race=White,capital-loss=None} |
#> {capital-loss=None,native-country=United-States} .
#> {capital-gain=None,native-country=United-States} .
#> {capital-gain=None,capital-loss=None} .
#> {capital-loss=None} .
#> {capital-gain=None} .
#> {native-country=United-States} .
#> {race=White} .
#> {capital-loss=None,native-country=United-States}
#> {race=White,capital-loss=None} .
#> {capital-loss=None,native-country=United-States} |
#> {capital-gain=None,native-country=United-States} .
#> {capital-gain=None,capital-loss=None} .
#> {capital-loss=None} .
#> {capital-gain=None} .
#> {native-country=United-States} .
#> {race=White} .
#> {capital-gain=None,native-country=United-States}
#> {race=White,capital-loss=None} .
#> {capital-loss=None,native-country=United-States} .
#> {capital-gain=None,native-country=United-States} |
#> {capital-gain=None,capital-loss=None} .
#> {capital-loss=None} .
#> {capital-gain=None} .
#> {native-country=United-States} .
#> {race=White} .
#> {capital-gain=None,capital-loss=None}
#> {race=White,capital-loss=None} .
#> {capital-loss=None,native-country=United-States} .
#> {capital-gain=None,native-country=United-States} .
#> {capital-gain=None,capital-loss=None} |
#> {capital-loss=None} .
#> {capital-gain=None} .
#> {native-country=United-States} .
#> {race=White} .
#> {capital-loss=None}
#> {race=White,capital-loss=None} |
#> {capital-loss=None,native-country=United-States} |
#> {capital-gain=None,native-country=United-States} .
#> {capital-gain=None,capital-loss=None} |
#> {capital-loss=None} |
#> {capital-gain=None} .
#> {native-country=United-States} .
#> {race=White} .
#> {capital-gain=None}
#> {race=White,capital-loss=None} .
#> {capital-loss=None,native-country=United-States} .
#> {capital-gain=None,native-country=United-States} |
#> {capital-gain=None,capital-loss=None} |
#> {capital-loss=None} .
#> {capital-gain=None} |
#> {native-country=United-States} .
#> {race=White} .
#> {native-country=United-States}
#> {race=White,capital-loss=None} .
#> {capital-loss=None,native-country=United-States} |
#> {capital-gain=None,native-country=United-States} |
#> {capital-gain=None,capital-loss=None} .
#> {capital-loss=None} .
#> {capital-gain=None} .
#> {native-country=United-States} |
#> {race=White} .
#> {race=White}
#> {race=White,capital-loss=None} |
#> {capital-loss=None,native-country=United-States} .
#> {capital-gain=None,native-country=United-States} .
#> {capital-gain=None,capital-loss=None} .
#> {capital-loss=None} .
#> {capital-gain=None} .
#> {native-country=United-States} .
#> {race=White} |
is.superset(set, set, sparse = FALSE)
#> {race=White,capital-loss=None}
#> {race=White,capital-loss=None} TRUE
#> {capital-loss=None,native-country=United-States} FALSE
#> {capital-gain=None,native-country=United-States} FALSE
#> {capital-gain=None,capital-loss=None} FALSE
#> {capital-loss=None} FALSE
#> {capital-gain=None} FALSE
#> {native-country=United-States} FALSE
#> {race=White} FALSE
#> {capital-loss=None,native-country=United-States}
#> {race=White,capital-loss=None} FALSE
#> {capital-loss=None,native-country=United-States} TRUE
#> {capital-gain=None,native-country=United-States} FALSE
#> {capital-gain=None,capital-loss=None} FALSE
#> {capital-loss=None} FALSE
#> {capital-gain=None} FALSE
#> {native-country=United-States} FALSE
#> {race=White} FALSE
#> {capital-gain=None,native-country=United-States}
#> {race=White,capital-loss=None} FALSE
#> {capital-loss=None,native-country=United-States} FALSE
#> {capital-gain=None,native-country=United-States} TRUE
#> {capital-gain=None,capital-loss=None} FALSE
#> {capital-loss=None} FALSE
#> {capital-gain=None} FALSE
#> {native-country=United-States} FALSE
#> {race=White} FALSE
#> {capital-gain=None,capital-loss=None}
#> {race=White,capital-loss=None} FALSE
#> {capital-loss=None,native-country=United-States} FALSE
#> {capital-gain=None,native-country=United-States} FALSE
#> {capital-gain=None,capital-loss=None} TRUE
#> {capital-loss=None} FALSE
#> {capital-gain=None} FALSE
#> {native-country=United-States} FALSE
#> {race=White} FALSE
#> {capital-loss=None}
#> {race=White,capital-loss=None} TRUE
#> {capital-loss=None,native-country=United-States} TRUE
#> {capital-gain=None,native-country=United-States} FALSE
#> {capital-gain=None,capital-loss=None} TRUE
#> {capital-loss=None} TRUE
#> {capital-gain=None} FALSE
#> {native-country=United-States} FALSE
#> {race=White} FALSE
#> {capital-gain=None}
#> {race=White,capital-loss=None} FALSE
#> {capital-loss=None,native-country=United-States} FALSE
#> {capital-gain=None,native-country=United-States} TRUE
#> {capital-gain=None,capital-loss=None} TRUE
#> {capital-loss=None} FALSE
#> {capital-gain=None} TRUE
#> {native-country=United-States} FALSE
#> {race=White} FALSE
#> {native-country=United-States}
#> {race=White,capital-loss=None} FALSE
#> {capital-loss=None,native-country=United-States} TRUE
#> {capital-gain=None,native-country=United-States} TRUE
#> {capital-gain=None,capital-loss=None} FALSE
#> {capital-loss=None} FALSE
#> {capital-gain=None} FALSE
#> {native-country=United-States} TRUE
#> {race=White} FALSE
#> {race=White}
#> {race=White,capital-loss=None} TRUE
#> {capital-loss=None,native-country=United-States} FALSE
#> {capital-gain=None,native-country=United-States} FALSE
#> {capital-gain=None,capital-loss=None} FALSE
#> {capital-loss=None} FALSE
#> {capital-gain=None} FALSE
#> {native-country=United-States} FALSE
#> {race=White} TRUE