Support Counting for Itemsets
Provides the generic function and the needed S4 method to count support for given itemsets (and other types of associations) in a given transaction database.
support(x, transactions, ...) ## S4 method for signature 'itemMatrix' support(x, transactions, type= c("relative", "absolute"), weighted = FALSE, control = NULL) ## S4 method for signature 'associations' support(x, transactions, type= c("relative", "absolute"), weighted = FALSE, control = NULL)
x |
the set of itemsets for which support should be counted. |
... |
further arguments are passed on. |
transactions |
the transaction data set used for mining. |
type |
a character string specifying
if |
weighted |
should support be weighted by transactions weights stored
as column |
control |
a named list with elements
|
Normally, itemset support is counted during mining the database with a set minimum support. However, if only the support information for a single or a few itemsets is needed, one might not want to mine the database for all frequent itemsets.
If in control method = "ptree"
is used,
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 tid list intersection.
If in control method = "tidlists"
is used,
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.
If in control reduce = TRUE
is used, unused items are removed from
the data before creating rules. This might be slower for large transaction
data sets.
A numeric vector of the same length as x
containing
the support values for the sets in x
.
Michael Hahsler and Christian Buchta
Michael Hahsler, Christian Buchta, and Kurt Hornik. Selective association rule generation. Computational Statistics, 23(2):303-315, April 2008.
data("Income") ## find and some frequent itemsets itemsets <- eclat(Income)[1:5] ## inspect the support returned by eclat inspect(itemsets) ## count support in the database support(items(itemsets), Income)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.