measures {arules} | R Documentation |
Provides the generic functions and the needed S4 methods to calculate some additional interest measures for a set of existing associations.
all_confidence(x, ...) ## S4 method for signature 'itemsets': all_confidence(x, transactions = NULL, itemSupport = NULL) hyperlift(x, ...) ## S4 method for signature 'rules': hyperlift(x, transactions, d = 0.99)
x |
the set of associations. |
... |
further arguments are passed on. |
transactions |
the transaction data set used to mine the associations. |
itemSupport |
alternatively to transactions, for some measures a item support in the transaction data set is sufficient. |
d |
the quantile used to calculate hyperlift. |
Currently the interest measures all-confidence and hyperlift are implemented.
All-confidence is defined on itemsets as the minimum confidence of all possible rule generated from the itemset.
Hyperlift is an adaptation of the lift measure which is more robust for low counts.
A vector containing the values of the interest measure for each association
in the set of associations x
.
Michael Hahsler
Edward R. Omiecinski. Alternative interest measures for mining associations in databases. IEEE Transactions on Knowledge and Data Engineering, 15(1):57-69, Jan/Feb 2003.
Michael Hahsler, Kurt Hornik, and Thomas Reutterer. Implications of probabilistic data modeling for rule mining. Report 14, Research Report Series, Department of Statistics and Mathematics, Wirschaftsuniversität Wien, Augasse 2-6, 1090 Wien, Austria, March 2005.
data("Income_transactions") ### calculate all-confidence itemsets <- apriori(Income_transactions, parameter = list(target = "freq")) quality(itemsets) <- cbind(quality(itemsets), all_confonfidence = all_confidence(itemsets)) summary(itemsets) ### calculate hyperlift for the 0.9 quantile rules <- apriori(Income_transactions) quality(rules) <- cbind(quality(rules), hyperlift = hyperlift(rules, Income_transactions, d = 0.9)) inspect(SORT(rules, by = "hyperlift")[1:5])