Find for each itemset in an associations object which transactions support (i.e., contains all items in the itemset) it. The information is returned as a tidLists object.
Usage
supportingTransactions(x, transactions, ...)
# S4 method for class 'associations'
supportingTransactions(x, transactions)Arguments
- x
a set of associations (itemsets, rules, etc.)
- transactions
an object of class transactions used to mine the associations in
x.- ...
currently unused.
Value
An object of class tidLists containing one transaction ID
list per association in x.
See also
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(),
sets,
size(),
tidLists-class,
transactions-class,
unique()
Examples
data <- list(
c("a", "b", "c"),
c("a", "b"),
c("a", "b", "d"),
c("b", "e"),
c("b", "c", "e"),
c("a", "d", "e"),
c("a", "c"),
c("a", "b", "d"),
c("c", "e"),
c("a", "b", "d", "e")
)
data <- as(data, "transactions")
## mine itemsets
f <- eclat(data, parameter = list(support = .2, minlen = 3))
#> Eclat
#>
#> parameter specification:
#> tidLists support minlen maxlen target ext
#> FALSE 0.2 3 10 frequent itemsets TRUE
#>
#> algorithmic control:
#> sparse sort verbose
#> 7 -2 TRUE
#>
#> Absolute minimum support count: 2
#>
#> create itemset ...
#> set transactions ...[5 item(s), 10 transaction(s)] done [0.00s].
#> sorting and recoding items ... [5 item(s)] done [0.00s].
#> creating bit matrix ... [5 row(s), 10 column(s)] done [0.00s].
#> writing ... [2 set(s)] done [0.00s].
#> Creating S4 object ... done [0.00s].
inspect(f)
#> items support count
#> [1] {a, d, e} 0.2 2
#> [2] {a, b, d} 0.3 3
## find supporting Transactions
st <- supportingTransactions(f, data)
st
#> tidLists in sparse format with
#> 2 items/itemsets (rows) and
#> 10 transactions (columns)
as(st, "list")
#> $`{a,d,e}`
#> [1] 6 10
#>
#> $`{a,b,d}`
#> [1] 3 8 10
#>