Support Counting for Itemsets

Counts support for itemsets represented by an itemMatrix or an associations object in a transactions data set.

Usage

support(x, transactions, ...)

# S4 method for class 'itemMatrix'
support(
  x,
  transactions,
  type = c("relative", "absolute"),
  method = c("ptree", "tidlists"),
  reduce = FALSE,
  weighted = FALSE,
  verbose = FALSE,
  ...
)

# S4 method for class 'associations'
support(
  x,
  transactions,
  type = c("relative", "absolute"),
  method = c("ptree", "tidlists"),
  reduce = FALSE,
  weighted = FALSE,
  verbose = FALSE,
  ...
)

Arguments

x: an itemMatrix or associations object containing the itemsets for which support is counted.
transactions: the transactions data set in which support is counted.
...: further arguments passed from the generic to a method.
type: return "relative" support or "absolute" counts (or summed weights when weighted = TRUE).
method: support-counting method: "ptree" or "tidlists".
reduce: logical; remove unused items before prefix-tree counting?
weighted: logical; use transaction weights stored in the weight column of transactionInfo()?
verbose: logical; report progress and timing information?

Value

An unnamed numeric vector of length length(x). Values are relative supports when type = "relative" and counts or weight sums when type = "absolute".

Details

Normally, the support of frequent itemsets is counted efficiently during the mining process using a minimum support threshold. However, if only the support for specific itemsets (maybe itemsets with very low support) is needed, or the support of a set of itemsets needs to be recalculated on different transactions than they were mined on, then support() can be used.

Several methods for support counting are available:

"ptree" (default method): The counters for the itemsets are organized in a prefix tree. The transactions are sequentially processed and the corresponding counters in the prefix tree are incremented (see Hahsler et al, 2008). This method is used by default since it is typically significantly faster than transaction ID list intersection.
"tidlists": Support is counted using transaction ID list intersection which is used by several fast mining algorithms (e.g., by Eclat). However, support is determined for each itemset individually which is slow for a large number of long itemsets in dense data.

The item coding of x and transactions is reconciled using item labels. Items that occur only in transactions do not affect the count. With reduce = TRUE, unused items are removed before prefix-tree counting.

Weighted support uses the numeric weight column in transactionInfo(transactions). Absolute weighted support is the sum of the weights of supporting transactions; relative weighted support divides this value by the sum of all transaction weights.

References

Michael Hahsler, Christian Buchta, and Kurt Hornik. Selective association rule generation. Computational Statistics, 23(2):303-315, April 2008.

Author

Michael Hahsler and Christian Buchta

Examples

data("Income")

## find and some frequent itemsets
itemsets <- eclat(Income)[1:5]
#> Eclat
#> 
#> parameter specification:
#>  tidLists support minlen maxlen            target  ext
#>     FALSE     0.1      1     10 frequent itemsets TRUE
#> 
#> algorithmic control:
#>  sparse sort verbose
#>       7   -2    TRUE
#> 
#> Absolute minimum support count: 687 
#> 
#> create itemset ... 
#> set transactions ...[50 item(s), 6876 transaction(s)] done [0.00s].
#> sorting and recoding items ... [30 item(s)] done [0.00s].
#> creating bit matrix ... [30 row(s), 6876 column(s)] done [0.00s].
#> writing  ... [5571 set(s)] done [0.00s].
#> Creating S4 object  ... done [0.00s].

## inspect the support returned by eclat
inspect(itemsets)
#>     items                              support count
#> [1] {occupation=clerical/service,                   
#>      language in home=english}       0.1127109   775
#> [2] {education=no college graduate,                 
#>      ethnic classification=hispanic} 0.1096568   754
#> [3] {marital status=married,                        
#>      dual incomes=no,                               
#>      householder status=own,                        
#>      language in home=english}       0.1007853   693
#> [4] {dual incomes=no,                               
#>      householder status=own,                        
#>      language in home=english}       0.1019488   701
#> [5] {marital status=married,                        
#>      dual incomes=no,                               
#>      householder status=own}         0.1058755   728

## count support in the database
support(items(itemsets), Income)
#> [1] 0.1127109 0.1096568 0.1007853 0.1019488 0.1058755
support(itemsets, Income, type = "absolute")
#> [1] 775 754 693 701 728