Subsetting Itemsets, Rules and Transactions

Provides the generic function subset() and methods to subset associations or transactions (itemMatrix) which meet certain conditions (e.g., contains certain items or satisfies a minimum lift).

Usage

subset(x, ...)

# S4 method for class 'itemMatrix'
subset(x, subset, ...)

# S4 method for class 'itemsets'
subset(x, subset, ...)

# S4 method for class 'rules'
subset(x, subset, ...)

Arguments

x

object to be subsetted.

...

further arguments to be passed to or from other methods.

subset

logical expression indicating elements to keep.

Value

An object of the same class as x containing only the elements which satisfy the conditions.

Details

subset() finds the rows/itemsets/rules of x that match the expression given in subset. Parts of x like items, lhs, rhs and the columns in the quality data.frame (e.g., support and lift) can be directly referred to by their names in subset.

Important operators to select itemsets containing items specified by their labels are

• %in%: select itemsets matching any given item

• %ain%: select only itemsets matching all given item

• %oin%: select only itemsets matching only the given item

• %pin%: %in% with partial matching

Author

Michael Hahsler

Examples

data("Adult")
rules <- 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].

## select all rules with item "marital-status=Never-married" in
## the right-hand-side and lift > 2
rules.sub <- subset(rules, subset = rhs %in% "marital-status=Never-married" &
  lift > 2)

## use partial matching for all items corresponding to the variable
## "marital-status"
rules.sub <- subset(rules, subset = rhs %pin% "marital-status=")

## select only rules with items "age=Young" and "workclass=Private" in
## the left-hand-side
rules.sub <- subset(rules, subset = lhs %ain%
  c("age=Young", "workclass=Private"))